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Watier, Nicholas. "Metacognitive Aspects of Face Identification." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/20532.
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To date, relatively little research has investigated participants’ ability to monitor their memory for faces and names. Four experiments were conducted with aim of developing a comprehensive profile of memory monitoring performance during face identification tasks. In each experiment, memory monitoring judgements were solicited during encoding and/or retrieval of unfamiliar face-name pairs. In general, subjective estimates of future and past memory performance were valid predictors of objective memory performance, regardless of whether a face or name was the item to be retrieved from memory. As a test of the stability of memory monitoring accuracy across different categories of stimuli, memory monitoring for face-name pairs was compared with noun-noun pairs. The predictive validity of estimates of future memory performance was similar across the categories of stimuli, but the predictive validity of estimates of past memory performance was superior for nouns compared with names.
A subset of the studies examined the influence of face and name distinctiveness on memory and memory monitoring for face-name associations. This was done in an attempt to identify sources of information that individuals might use to monitor their memory during face-name learning. The beneficial effects of distinctiveness on associative memory were symmetrical between faces and names, such that relative to their typical counterparts, distinct faces enhanced memory for names, and distinct names enhanced memory for faces. These effects were also apparent in memory monitoring. Estimates of future and past memory performance were greater for face-name associations that contained a distinct face or name compared with a typical face or name, regardless of whether the distinct item was a cue or target. Moreover, the predictive validity of prospective monitoring improved with name distinctiveness, whereas the predictive validity of retrospective monitoring improved with facial distinctiveness. Altogether, the results of the dissertation indicate that participants can monitor their memory for faces and names at a level above chance, that retrospective metamemory is more accurate for nouns compared with names, and that distinctiveness not only affects the strength of the association between a face and a name, but also the ability to monitor that association.
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McIntyre, A. H. "Applying psychology to forensic facial identification : perception and identification of facial composite images and facial image comparison." Thesis, University of Stirling, 2012. http://hdl.handle.net/1893/9077.
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Eyewitness recognition is acknowledged to be prone to error but there is less understanding of difficulty in discriminating unfamiliar faces. This thesis examined the effects of face perception on identification of facial composites, and on unfamiliar face image comparison. Facial composites depict face memories by reconstructing features and configurations to form a likeness. They are generally reconstructed from an unfamiliar face memory, and will be unavoidably flawed. Identification will require perception of any accurate features, by someone who is familiar with the suspect and performance is typically poor. In typical face perception, face images are processed efficiently as complete units of information. Chapter 2 explored the possibility that holistic processing of inaccurate composite configurations will impair identification of individual features. Composites were split below the eyes and misaligned to impair holistic analysis (cf. Young, Hellawell, & Jay, 1987); identification was significantly enhanced, indicating that perceptual expertise with inaccurate configurations exerts powerful effects that can be reduced by enabling featural analysis. Facial composite recognition is difficult, which means that perception and judgement will be influence by an affective recognition bias: smiles enhance perceived familiarity, while negative expressions produce the opposite effect. In applied use, facial composites are generally produced from unpleasant memories and will convey negative expression; affective bias will, therefore, be important for facial composite recognition. Chapter 3 explored the effect of positive expression on composite identification: composite expressions were enhanced, and positive affect significantly increased identification. Affective quality rather than expression strength mediated the effect, with subtle manipulations being very effective. Facial image comparison (FIC) involves discrimination of two or more face images. Accuracy in unfamiliar face matching is typically in the region of 70%, and as discrimination is difficult, may be influenced by affective bias. Chapter 4 explored the smiling face effect in unfamiliar face matching. When multiple items were compared, positive affect did not enhance performance and false positive identification increased. With a delayed matching procedure, identification was not enhanced but in contrast to face recognition and simultaneous matching, positive affect improved rejection of foil images. Distinctive faces are easier to discriminate. Chapter 5 evaluated a systematic caricature transformation as a means to increase distinctiveness and enhance discrimination of unfamiliar faces. Identification of matching face images did not improve, but successful rejection of non-matching items was significantly enhanced. Chapter 6 used face matching to explore the basis of own race bias in face perception. Other race faces were manipulated to show own race facial variation, and own race faces to show African American facial variation. When multiple face images were matched simultaneously, the transformation impaired performance for all of the images; but when images were individually matched, the transformation improved perception of other race faces and discrimination of own race faces declined. Transformation of Japanese faces to show own race dimensions produced the same pattern of effects but failed to reach significance. The results provide support for both perceptual expertise and featural processing theories of own race bias. Results are interpreted with reference to face perception theories; implications for application and future study are discussed.
3
Kranauskas, Justas. "Person Identification by Face and Iris." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20100213_102103-34345.
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In this thesis, person identification by combining automatic face and iris recognition is analyzed. Person identification by his face is one of the most intuitive from all biometric measures. We are used to recognizing familiar faces and confirming identity by a short glance at one's id card which contains image of the face. We are also used to being observed by surveillance cameras, which can perform biometric authentication without even being noticed. However, facial biometrics is one of most unstable metrics because the face gets noticeably older in several years and can frequently change depending on the mood of its owner. The core algorithm for facial recognition presented in this work is based on Gabor features. Deep analysis of each step helped to develop the method with better or similar accuracy to the best published results received on the same datasets, while being simple and fast.
On the other hand, person identification by his iris is one of the most sophisticated, stable and accurate biometrics. The core algorithm for iris recognition presented in this work is based on a novel iris texture representation by local extremum points of multiscale Taylor expansion. The proposed irises comparison method is very different from the classic phase-based methods, but is also fast and accurate. Combining it with our implementation of phase-based method results in superior recognition accuracy which is comparable or better than any published results received on the same... [to full text]Darbe tyrinėjama asmens identifikacija, kombinuojant automatinį veido ir akies rainelės atpažinimą. Automatinė identifikacija pagal veidą yra intuityviausia iš biometrijos metrikų, kadangi būtent pagal veidą mes geriausiai sugebame atpažinti pažįstamus asmenis. Tai yra ir viena labiausiai priimtinų, kadangi visi esame įprate, kad mus filmuoja apsaugos kameros, lengviausiai išmatuojama - nes nereikalauja jokių įmantrių skanerių, tačiau kartu - tai yra ir viena iš nestabiliausių metrikų, kadangi veidas sensta ir šiaip kinta priklausomai nuo savininko nuotaikos. Darbe pristatomas veidų atpažinimo algoritmas paremtas Gaboro požymiais. Nuodugni analizė padėjo sukurti algoritmą, kurio tikslumą vertinant standartiniais testais jis lenkia arba yra lygus su geriausiais publikuotais metodais, tačiau pasižymi paprastumu ir dideliu greičiu. Tuo tarpu automatinė identifikacija pagal rainelę yra laikoma viena stabiliausių ir tiksliausių. Darbe pristatomas rainelių atpažinimo algoritmas naudoja naujovišką rainelių tekstūros vaizdavimo būdą, paremtą lokaliais dvimačiais funkcijų aproksimacijos Teiloro eilutėmis ekstremumais. Kartu pristatomas naudojamų požymių palyginimo metodas, kuris yra labai nutolęs nuo bet kokių iki šiol rainelių tekstūrų palyginimui naudojamų metodų. Pasiūlytas rainelių atpažinimo metodas vėlgi yra spartus ir itin tikslus, o sujungus su klasikinio stiliaus rainelių tekstūrų palyginimu tikslumu nenusileidžia geriausiems publikuotiems metodams. Darbas užbaigiamas veidų... [toliau žr. visą tekstą]
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Stone, Zachary. "Face Identification in the Internet Era." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10397.
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Despite decades of effort in academia and industry, it is not yet possible to build machines that can replicate many seemingly-basic human perceptual abilities. This work focuses on the problem of face identification that most of us effortlessly solve daily. Substantial progress has been made towards the goal of automatically identifying faces under tightly controlled conditions; however, in the domain of unconstrained face images, many challenges remain. We observe that the recent combination of widespread digital photography, inexpensive digital storage and bandwidth, and online social networks has led to the sudden creation of repositories of billions of shared photographs and opened up an important new domain for unconstrained face identification research. Drawing upon the newly-popular phenomenon of “tagging,” we construct some of the first face identification datasets that are intended to model the digital social spheres of online social network members, and we examine various qualitative and quantitative properties of these image sets. The identification datasets we present here include up to 100 individuals, making them comparable to the average size of members’ networks of “friends” on a popular online social network, and each individual is represented by up to 100 face samples that feature significant real-world variation in appearance, expression, and pose. We demonstrate that biologically-inspired visual representations can achieve state-of-the-art face identification performance on our novel frontal and multi-pose face datasets. We also show that the addition of a tree-structured classifier and training set augmentation can enhance accuracy in the multi-pose setting. Finally, we illustrate that the machine-readable “social context” in which shared photos are often embedded can be applied to further boost face identification accuracy. Taken together, our results suggest that accurate automated face identification in vast online shared photo collections is now feasible.Engineering and Applied Sciences
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Wong, Vincent. "Human face recognition /." Online version of thesis, 1994. http://hdl.handle.net/1850/11882.
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Jovanović, Nataša. "To whom it may concern adressee identification in face-to-face meetings /." Enschede : University of Twente [Host], 2007. http://doc.utwente.nl/57870.
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Morrison, Donald J. "Positive and negative priming of person identification." Thesis, University of Stirling, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244519.
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Brennen, Tim. "Facilitation and inhibition of person identification." Thesis, University of Nottingham, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.329857.
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Knuycky, Leslie Riddick. "The Cross Race Effect: The Influence of Stereotypicality on Memory Errors." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/psych_theses/66.
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In eyewitness identification cases, suspect misidentification is the leading factor attributed to wrongful convictions (Scheck, Neufeld, & Dwyer, 2000), thus, it is of applied importance to identify factors that contribute to the false recollection of faces. One potential factor addressed in the current study was whether face memory and subsequent identification for other-race-faces is biased by the degree to which a target face posses facial features associated with ethnic identity. Individual differences in level of processing (global, local) and prejudice were tested as potential mechanisms contributing to biased judgments. In Experiment 1 a standard face recognition task revealed that prejudice, level of processing, and face-type interacted to predict recognition bias. In Experiment 2 results showed that positive misidentifications (i.e., choosing an incorrect foil) were more likely when a stereotypical versus non-stereotypical Black actor was witnessed committing the crime. Results are discussed in terms of theoretical and practical implications.
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Edmunds, Taiamiti. "Protection of 2D face identification systems against spoofing attacks." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAT007/document.
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Les systèmes d’identification faciale sont en plein essor et se retrouvent de plus en plus dans des produits grand public tels que les smartphones et les ordinateurs portables. Cependant, ces systèmes peuvent être facilement bernés par la présentation par exemple d’une photo imprimée de la personne ayant les droits d’accès au système. Cette thèse s’inscrit dans le cadre du projet ANR BIOFENCE qui vise à développer une certification des systèmes biométriques veine, iris et visage permettant aux industriels de faire valoir leurs innovations en termes de protection. L’objectif de cette thèse est double, d’abord il s’agit de développer des mesures de protection des systèmes 2D d’identification faciale vis à vis des attaques connues à ce jour (photos imprimées, photos ou vidéos sur un écran, masques) puis de les confronter à la méthodologie de certification développée au sein du projet ANR. Dans un premier temps, un état de l’art général des attaques et des contremesures est présenté en mettant en avant les méthodes algorithmiques (« software ») par rapport aux méthodes hardware. Ensuite, plusieurs axes sont approfondis au cours de ce travail. Le premier concerne le développement d’une contremesure basée sur une analyse de texture et le second concerne le développement d’une contre-mesure basée sur une analyse de mouvement. Ensuite, une modélisation du processus de recapture pour différencier un faux visage d’un vrai est proposée. Une nouvelle méthode de protection est développée sur ce concept en utilisant les données d'enrolment des utilisateurs et un premier pas est franchi dans la synthèse d'attaque pour un nouvel utilisateur à partir de sa donnée d'enrolment. Enfin, la méthodologie de certification développée pour les systèmes à empreintes digitales est évaluée pour les systèmes d'identification facialFace identification systems are growing rapidly and invade the consumer market with security products in smartphones, computers and banking. However, these systems are easily fooled by presenting a picture of the person having legitimate access to the system. This thesis is part of the BIOFENCE project which aim to develop a certification of biometric systems in order for industrials to promote their innovations in terms of protection. Our goal is to develop new anti-spoofing countermeasures for 2D face biometric systems and to evaluate the certification methodology on protected systems. First, a general state of the art in face spoofing attack forgery and in anti-spoofing protection measures is presented. Then texture-based countermeasures and motion-based countermeasures are investigated leading to the development of two novel countermeasures. Then, the recapturing process is modelled and a new fake face detection approach is proposed based on this model. Taking advantage of enrolment samples from valid users, a first step toward the synthesis of spoofing attacks for new users is taken. Finally, the certification methodology originally developed for fingerprint technology is evaluated on face biometric systems
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Saleh, Mohamed Ibrahim. "Using Ears for Human Identification." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/33158.
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Biometrics includes the study of automatic methods for distinguishing human beings based on physical or behavioral traits. The problem of finding good biometric features and recognition methods has been researched extensively in recent years. Our research considers the use of ears as a biometric for human recognition. Researchers have not considered this biometric as much as others, which include fingerprints, irises, and faces. This thesis presents a novel approach to recognize individuals based on their outer ear images through spatial segmentation. This approach to recognizing is also good for dealing with occlusions. The study will present several feature extraction techniques based on spatial segmentation of the ear image. The study will also present a method for classifier fusion. Principal components analysis (PCA) is used in this research for feature extraction and dimensionality reduction. For classification, nearest neighbor classifiers are used. The research also investigates the use of ear images as a supplement to face images in a multimodal biometric system. Our base eigen-ear experiment results in an 84% rank one recognition rate, and the segmentation method yielded improvements up to 94%. Face recognition by itself, using the same approach, gave a 63% rank one recognition rate, but when complimented with ear images in a multimodal system improved to 94% rank one recognition rate.Master of Science
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Bennetts, Jennifer. "The identification and characterisation of novel genes in development /." [St. Lucia, Qld.], 2006. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe19375.pdf.
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Jonsson, K. T. "Robust correlation and support vector machines for face identification." Thesis, University of Surrey, 2000. http://epubs.surrey.ac.uk/799/.
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Li, Yongping. "Linear discriminant analysis and its application to face identification." Thesis, University of Surrey, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326513.
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Nguyen, Thanh Kien. "Human identification at a distance using iris and face." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/62876/1/Kien_Nguyen%20Thanh_Thesis.pdf.
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This research has successfully applied super-resolution and multiple modality fusion techniques to address the major challenges of human identification at a distance using face and iris. The outcome of the research is useful for security applications.
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Man, Chun Him. "Human face image searching system with relevance feedback using sketch." HKBU Institutional Repository, 2005. http://repository.hkbu.edu.hk/etd_ra/618.
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Lal, Sulakshana. "Identification, Characterisation and Prioritisation of Student Interactions in Face-to-Face and Remotely-operated Engineering Laboratories." Thesis, Curtin University, 2020. http://hdl.handle.net/20.500.11937/86949.
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There are various modes of conducting laboratory activities in university-level engineering education. The present study focuses upon those which involve students' operation of real physical equipment in their conduct of laboratory activity, that is, face-to-face and remotely-operated laboratories. This study first identifies and characterises student interactions that underpin laboratory learning in both face-to-face and remotely-operated laboratories and then prioritises such interactions to make recommendations for transfer from face-to-face to create effective remotely-operated laboratories.
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Cowle, Kenneth M. "Accuracy Variations in Human Facial Identification Based on Time of Exposure." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5415/.
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This study examined the relationship between time of exposure to the human face and accurate subsequent photo line-up identification. A volunteer group of 124 undergraduate students was divided into three approximately equal sized subgroups. The three groups were then exposed to a video or a portion of a video depicting a theft. Exposure times ranged from two minutes to 30 seconds. The subjects were then given a questionnaire and shown a photo line-up of the mock perpetrator and five foils. Subjects were asked to identify the perpetrator and mark that identification on the questionnaire. Results of the experiment indicated that the longer a subject was exposed the greater the possibility of an accurate identification.
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Singh, Richa. "Mitigating the effect of covariates in face recognition." Morgantown, W. Va. : [West Virginia University Libraries], 2008. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5990.
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Thesis (Ph. D.)--West Virginia University, 2008.Title from document title page. Document formatted into pages; contains xv, 136 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 125-136).
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Cadavid, Steven. "Human Identification Based on Three-Dimensional Ear and Face Models." Scholarly Repository, 2011. http://scholarlyrepository.miami.edu/oa_dissertations/516.
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We propose three biometric systems for performing 1) Multi-modal Three-Dimensional (3D) ear + Two-Dimensional (2D) face recognition, 2) 3D face recognition, and 3) hybrid 3D ear recognition combining local and holistic features. For the 3D ear component of the multi-modal system, uncalibrated video sequences are utilized to recover the 3D ear structure of each subject within a database. For a given subject, a series of frames is extracted from a video sequence and the Region-of-Interest (ROI) in each frame is independently reconstructed in 3D using Shape from Shading (SFS). A fidelity measure is then employed to determine the model that most accurately represents the 3D structure of the subject’s ear. Shape matching between a probe and gallery ear model is performed using the Iterative Closest Point (ICP) algorithm. For the 2D face component, a set of facial landmarks is extracted from frontal facial images using the Active Shape Model (ASM) technique. Then, the responses of the facial images to a series of Gabor filters at the locations of the facial landmarks are calculated. The Gabor features are stored in the database as the face model for recognition. Match-score level fusion is employed to combine the match scores obtained from both the ear and face modalities. The aim of the proposed system is to demonstrate the superior performance that can be achieved by combining the 3D ear and 2D face modalities over either modality employed independently. For the 3D face recognition system, we employ an Adaboost algorithm to builda classifier based on geodesic distance features. Firstly, a generic face model is finely conformed to each face model contained within a 3D face dataset. Secondly, the geodesic distance between anatomical point pairs are computed across each conformed generic model using the Fast Marching Method. The Adaboost algorithm then generates a strong classifier based on a collection of geodesic distances that are most discriminative for face recognition. The identification and verification performances of three Adaboost algorithms, namely, the original Adaboost algorithm proposed by Freund and Schapire, and two variants – the Gentle and Modest Adaboost algorithms – are compared. For the hybrid 3D ear recognition system, we propose a method to combine local and holistic ear surface features in a computationally efficient manner. The system is comprised of four primary components, namely, 1) ear image segmentation, 2) local feature extraction and matching, 3) holistic feature extraction and matching, and 4) a fusion framework combining local and holistic features at the match score level. For the segmentation component, we employ our method proposed in [111], to localize a rectangular region containing the ear. For the local feature extraction and representation component, we extend the Histogram of Categorized Shapes (HCS) feature descriptor, proposed in [111], to an object-centered 3D shape descriptor, termed Surface Patch Histogram of Indexed Shapes (SPHIS), for surface patch representation and matching. For the holistic matching component, we introduce a voxelization scheme for holistic ear representation from which an efficient, element-wise comparison of gallery-probe model pairs can be made. The match scores obtained from both the local and holistic matching components are fused to generate the final match scores. Experimental results conducted on the University of Notre Dame (UND) collection J2 dataset demonstrate that theproposed approach outperforms state-of-the-art 3D ear biometric systems in both accuracy and efficiency.
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Fayat, Vincent. "Approche et analyse des différentes méthodes de reconstitution faciale tridimensionnelle." Toulouse 3, 2001. http://www.theses.fr/2001TOU33025.
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Kičina, Pavol. "Automatická identifikace tváří v reálných podmínkách." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-218980.
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This master‘s thesis describes the identification faces in real terms. It includes an overview of current methods of detection faces by the classifiers. It also includes various methods for detecting faces. The second part is a description of two programs designed to identify persons. The first program operates in real time under laboratory conditions, where using web camera acquires images of user's face. This program is designed to speed recognition of persons. The second program has been working on static images, in real terms. The main essence of this method is successful recognition of persons, therefore the emphasis on computational complexity. The programs I used a staged method of PCA, LDA and kernel PCA (KPCA). The first program only works with the PCA method, which has good results with respect to the success and speed of recognition. In the second program to compare methods, which passed the best method for KPCA.
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Feng, Yicheng. "Template protecting algorithms for face recognition system." HKBU Institutional Repository, 2007. http://repository.hkbu.edu.hk/etd_ra/832.
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Akinbola, Akintunde A. "Estimation of image quality factors for face recognition." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4308.
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Thesis (M.S.)--West Virginia University, 2005.Title from document title page. Document formatted into pages; contains vi, 56 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 52-56).
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Manohar, Vasant. "Video-Based Person Identification Using Facial Strain Maps as a Biometric." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/3797.
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Research on video-based face recognition has started getting increased attention in the past few years. Algorithms developed for video have an advantage from the availability of plentitude of frames in videos to extract information from. Despite this fact, most research in this direction has limited the scope of the problem to the application of still image-based approaches to some selected frames on which 2D algorithms are expected to perform well. It can be realized that such an approach only uses the spatial information contained in video and does not incorporate the temporal structure.Only recently has the intelligence community begun to approach the problem in this direction. Video-based face recognition algorithms in the last couple of years attempt to simultaneously use the spatial and temporal information for the recognition of moving faces. A new face recognition method that falls into the category of algorithms that adopt spatio-temporal representation and utilizes dynamic information extracted from video is presented. The method was designed based on the hypothesis that the strain pattern exhibited during facial expression provides a unique "fingerprint" for recognition. First, a dense motion field is obtained with an optical flow algorithm. A strain pattern is then derived from the motion field. In experiments with 30 subjects, results indicate that strain pattern is an useful biometric, especially when dealing with extreme conditions such as shadow light and face camouflage, for which conventional face recognition methods are expected to fail. The ability to characterize the face using the elastic properties of facial skin opens up newer avenues to the face recognition community in the context of modeling a face using features beyond visible cues.
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Cheng, Xin. "Nonrigid face alignment for unknown subject in video." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/65338/1/Xin_Cheng_Thesis.pdf.
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Non-rigid face alignment is a very important task in a large range of applications but the existing tracking based non-rigid face alignment methods are either inaccurate or requiring person-specific model. This dissertation has developed simultaneous alignment algorithms that overcome these constraints and provide alignment with high accuracy, efficiency, robustness to varying image condition, and requirement of only generic model.
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Canavan, Shaun. "Face recognition by multi-frame fusion of rotating heads in videos /." Connect to resource online, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1210446052.
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Yaprakkaya, Gokhan. "Face Identification, Gender And Age Groups Classifications For Semantic Annotation Of Videos." Thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612848/index.pdf.
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This thesis presents a robust face recognition method and a combination of methods for gender identification and age group classification for semantic annotation of videos. Local binary pattern histogram which has 256 bins and pixel intensity differences are used as extracted facial features for gender classification. DCT Mod2 features and edge detection results around facial landmarks are used as extracted facial features for age group classification. In gender classification module, a Random Trees classifier is trained with LBP features and an adaboost classifier is trained with pixel intensity differences. DCT Mod2 features are used for training of a Random Trees classifier and LBP features around facial landmark points are used for training another Random Trees classifier in age group classification module. DCT Mod2 features of the detected faces morped by two dimensional face morphing method based on Active Appearance Model and Barycentric Coordinates are used as the inputs of the nearest neighbor classifier with weights obtained from the trained Random Forest classifier in face identification module. Different feature extraction methods are tried and compared and the best achievements in the face recognition module to be used in the method chosen. We compared our classification results with some successful earlier works results in our experiments performed with same datasets and got satisfactory results.
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Flowe, Heather D. "The effect of lineup member similarity on recognition accuracy in simultaneous and sequential lineups." Diss., Connected to a 24 p. preview or request complete full text in PDF format. Access restricted to UC IP addresses, 2005. http://wwwlib.umi.com/cr/ucsd/fullcit?p3189995.
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Thesis (Ph. D.)--University of California, San Diego, 2005.Title from first page of PDF file (viewed March 1, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references ( p. 113-116).
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Joyce, Carrie A. "Saving faces : using eye movement, ERP, and SCR measures of face processing and recognition to investigate eyewitness identification /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2000. http://wwwlib.umi.com/cr/ucsd/fullcit?p9984296.
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Louw, Lloyd A. B. "Automated face detection and recognition for a login system." Thesis, Link to the online version, 2007. http://hdl.handle.net/10019/438.
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Ndlangisa, Mboneli. "DRUBIS : a distributed face-identification experimentation framework - design, implementation and performance issues." Thesis, Rhodes University, 2004. http://eprints.ru.ac.za/93/1/MNdlangisa-MSc.pdf.
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We report on the design, implementation and performance issues of the DRUBIS (Distributed Rhodes University Biometric Identification System) experimentation framework. The Principal Component Analysis (PCA) face-recognition approach is used as a case study. DRUBIS is a flexible experimentation framework, distributed over a number of modules that are easily pluggable and swappable, allowing for the easy construction of prototype systems. Web services are the logical means of distributing DRUBIS components and a number of prototype applications have been implemented from this framework. Different popular PCA face-recognition related experiments were used to evaluate our experimentation framework. We extract recognition performance measures from these experiments. In particular, we use the framework for a more indepth study of the suitability of the DFFS (Difference From Face Space) metric as a means for image classification in the area of race and gender determination.
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RIBEIRO, LUIZ CLAUDIO. "IDENTIFICATION OF BOX AND JENKINS: A COPARISON BETWEEN FACE AND PADÉ APPROXIMATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1992. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9016@1.
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Desde de 1970, quando Box e Jenkins introduziram os
modelos ARMA para análise e previsão de séries
temporais,
muitos estudos foram desenvolvidos buscando encontrar um
método mais eficiente de identificação de tais modelos.
Tal fato se deu porque o método por Box e Jenkins,
baseado
na função de auto-correlação parcial (FACP) não são
eficientes quando os modelos apresentam componentes auto-
regressivas (AR) e médias móveis (MA).
Estudos comparativos realizados anteriormente mostraram
que dentre os métodos de identificação já desenvolvidos,
o
que se mostrou mais eficiente foi o baseado na função de
auto-correlação extendida (FACE) de TIAO e TSAY (1992)
Recentemente, Kuldeep Kumar introduziu na literatura um
método de identificação baseado na teoria de aproximação
de Padé. O objetivo deste trabalho é comparar o método
da
FACE com o método baseado na teoria de aproximação de
Padé.Since 1970, when Box and Jenkins first introduced the ARMA models to analysis and predict of time series data, a lot of studies have been developed to find an efficient identification method for such models. This was due the fact that the identification method proposed by Box and Jenkins, based on Auto-correlation Function (ACF) and Partial Auto-correlation Function (PACF), are inefficient when the models have auto regressive - AR- and moving average - MA- components. Comparative studies undertaken, have shown that, among the identification methods already developed, the method based on the Extended Auto-correlation Fuction of Tiao and Tsay (1982) is the most efficient. More recently, however, Kuldeep Kumar has introduced in the literature an identification method based on the theory of Padé aproximation. The objective of this paper is to compare the Extended Auto-correlation Function method with the method based on the Theory of Padé approximation.
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Chung, Cheuk-fai Bell, and 鍾灼輝. "Lineup superiority effects in cross-racial eyewitness identification." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45147607.
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Bredin, Hervé. "Vérification de l'identité d'un visage parlant : apport de la mesure de synchronie audiovisuelle face aux tentatives délibérées d'imposture /." Paris : École nationale supérieure des télécommunications, 2007. http://catalogue.bnf.fr/ark:/12148/cb41198293z.
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Chiachia, Giovani 1981. "Learning person-specific face representations = Aprendendo representações específicas para a face de cada pessoa." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275626.
Full textAbstract:
Orientadores: Alexandre Xavier Falcão, Anderson de Rezende RochaTese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação
Made available in DSpace on 2018-08-23T15:41:33Z (GMT). No. of bitstreams: 1 Chiachia_Giovani_D.pdf: 4376963 bytes, checksum: 8f7d18d591f2a5d943313d89416f96d4 (MD5) Previous issue date: 2013
Resumo: Os seres humanos são especialistas natos em reconhecimento de faces, com habilidades que excedem em muito as dos métodos automatizados vigentes, especialmente em cenários não controlados, onde não há a necessidade de colaboração por parte do indivíduo sendo reconhecido. No entanto, uma característica marcante do reconhecimento de face humano é que nós somos substancialmente melhores no reconhecimento de faces familiares, provavelmente porque somos capazes de consolidar uma grande quantidade de experiência prévia com a aparência de certo indivíduo e de fazer uso efetivo dessa experiência para nos ajudar no reconhecimento futuro. De fato, pesquisadores em psicologia têm até mesmo sugeridos que a representação interna que fazemos das faces pode ser parcialmente adaptada ou otimizada para rostos familiares. Enquanto isso, a situação análoga no reconhecimento facial automatizado | onde um grande número de exemplos de treinamento de um indivíduo está disponível | tem sido muito pouco explorada, apesar da crescente relevância dessa abordagem na era das mídias sociais. Inspirados nessas observações, nesta tese propomos uma abordagem em que a representação da face de cada pessoa é explicitamente adaptada e realçada com o intuito de reconhecê-la melhor. Apresentamos uma coleção de métodos de aprendizado que endereça e progressivamente justifica tal abordagem. Ao aprender e operar com representações específicas para face de cada pessoa, nós somos capazes de consistentemente melhorar o poder de reconhecimento dos nossos algoritmos. Em particular, nós obtemos resultados no estado da arte na base de dados PubFig83, uma desafiadora coleção de imagens instituída e tornada pública com o objetivo de promover o estudo do reconhecimento de faces familiares. Nós sugerimos que o aprendizado de representações específicas para face de cada pessoa introduz uma forma intermediária de regularização ao problema de aprendizado, permitindo que os classificadores generalizem melhor através do uso de menos |, porém mais relevantes | características faciais
Abstract: Humans are natural face recognition experts, far outperforming current automated face recognition algorithms, especially in naturalistic, \in-the-wild" settings. However, a striking feature of human face recognition is that we are dramatically better at recognizing highly familiar faces, presumably because we can leverage large amounts of past experience with the appearance of an individual to aid future recognition. Researchers in psychology have even suggested that face representations might be partially tailored or optimized for familiar faces. Meanwhile, the analogous situation in automated face recognition, where a large number of training examples of an individual are available, has been largely underexplored, in spite of the increasing relevance of this setting in the age of social media. Inspired by these observations, we propose to explicitly learn enhanced face representations on a per-individual basis, and we present a collection of methods enabling this approach and progressively justifying our claim. By learning and operating within person-specific representations of faces, we are able to consistently improve performance on both the constrained and the unconstrained face recognition scenarios. In particular, we achieve state-of-the-art performance on the challenging PubFig83 familiar face recognition benchmark. We suggest that such person-specific representations introduce an intermediate form of regularization to the problem, allowing the classifiers to generalize better through the use of fewer | but more relevant | face features
Doutorado
Ciência da Computação
Doutor em Ciência da Computação
37
Feng, Yicheng. "Discriminability and security of binary template in face recognition systems." HKBU Institutional Repository, 2012. https://repository.hkbu.edu.hk/etd_ra/1455.
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Ross, Stephen James. "Processing facial similarity utilizing denotative and connotative information to understand facial similarity judgments /." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.
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Topp, Lisa Dawn. "An evaluation of eyewitness decision making strategies for simultaneous and sequential lineups." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.
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Newlands, Pamela Jane. "Eyewitness interviewing : does the cognitive interview fit the bill?" Thesis, University of Westminster, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362662.
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El, Seuofi Sherif M. "Performance Evaluation of Face Recognition Using Frames of Ten Pose Angles." Youngstown State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1198184813.
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Jesse, Alexandra. "Towards a lexical fuzzy logical model of perception : the time-course of information in lexical identification of face-to face speech /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2005. http://uclibs.org/PID/11984.
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Tan, Teewoon. "HUMAN FACE RECOGNITION BASED ON FRACTAL IMAGE CODING." University of Sydney. Electrical and Information Engineering, 2004. http://hdl.handle.net/2123/586.
Full textAbstract:
Human face recognition is an important area in the field of biometrics. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. In this thesis we describe fully automatic solutions that can locate faces and then perform identification and verification. We present a solution for face localisation using eye locations. We derive an efficient representation for the decision hyperplane of linear and nonlinear Support Vector Machines (SVMs). For this we introduce the novel concept of $\rho$ and $\eta$ prototypes. The standard formulation for the decision hyperplane is reformulated and expressed in terms of the two prototypes. Different kernels are treated separately to achieve further classification efficiency and to facilitate its adaptation to operate with the fast Fourier transform to achieve fast eye detection. Using the eye locations, we extract and normalise the face for size and in-plane rotations. Our method produces a more efficient representation of the SVM decision hyperplane than the well-known reduced set methods. As a result, our eye detection subsystem is faster and more accurate. The use of fractals and fractal image coding for object recognition has been proposed and used by others. Fractal codes have been used as features for recognition, but we need to take into account the distance between codes, and to ensure the continuity of the parameters of the code. We use a method based on fractal image coding for recognition, which we call the Fractal Neighbour Distance (FND). The FND relies on the Euclidean metric and the uniqueness of the attractor of a fractal code. An advantage of using the FND over fractal codes as features is that we do not have to worry about the uniqueness of, and distance between, codes. We only require the uniqueness of the attractor, which is already an implied property of a properly generated fractal code. Similar methods to the FND have been proposed by others, but what distinguishes our work from the rest is that we investigate the FND in greater detail and use our findings to improve the recognition rate. Our investigations reveal that the FND has some inherent invariance to translation, scale, rotation and changes to illumination. These invariances are image dependent and are affected by fractal encoding parameters. The parameters that have the greatest effect on recognition accuracy are the contrast scaling factor, luminance shift factor and the type of range block partitioning. The contrast scaling factor affect the convergence and eventual convergence rate of a fractal decoding process. We propose a novel method of controlling the convergence rate by altering the contrast scaling factor in a controlled manner, which has not been possible before. This helped us improve the recognition rate because under certain conditions better results are achievable from using a slower rate of convergence. We also investigate the effects of varying the luminance shift factor, and examine three different types of range block partitioning schemes. They are Quad-tree, HV and uniform partitioning. We performed experiments using various face datasets, and the results show that our method indeed performs better than many accepted methods such as eigenfaces. The experiments also show that the FND based classifier increases the separation between classes. The standard FND is further improved by incorporating the use of localised weights. A local search algorithm is introduced to find a best matching local feature using this locally weighted FND. The scores from a set of these locally weighted FND operations are then combined to obtain a global score, which is used as a measure of the similarity between two face images. Each local FND operation possesses the distortion invariant properties described above. Combined with the search procedure, the method has the potential to be invariant to a larger class of non-linear distortions. We also present a set of locally weighted FNDs that concentrate around the upper part of the face encompassing the eyes and nose. This design was motivated by the fact that the region around the eyes has more information for discrimination. Better performance is achieved by using different sets of weights for identification and verification. For facial verification, performance is further improved by using normalised scores and client specific thresholding. In this case, our results are competitive with current state-of-the-art methods, and in some cases outperform all those to which they were compared. For facial identification, under some conditions the weighted FND performs better than the standard FND. However, the weighted FND still has its short comings when some datasets are used, where its performance is not much better than the standard FND. To alleviate this problem we introduce a voting scheme that operates with normalised versions of the weighted FND. Although there are no improvements at lower matching ranks using this method, there are significant improvements for larger matching ranks. Our methods offer advantages over some well-accepted approaches such as eigenfaces, neural networks and those that use statistical learning theory. Some of the advantages are: new faces can be enrolled without re-training involving the whole database; faces can be removed from the database without the need for re-training; there are inherent invariances to face distortions; it is relatively simple to implement; and it is not model-based so there are no model parameters that need to be tweaked.
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Tan, Teewoon. "HUMAN FACE RECOGNITION BASED ON FRACTAL IMAGE CODING." Thesis, The University of Sydney, 2003. http://hdl.handle.net/2123/586.
Full textAbstract:
Human face recognition is an important area in the field of biometrics. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. In this thesis we describe fully automatic solutions that can locate faces and then perform identification and verification. We present a solution for face localisation using eye locations. We derive an efficient representation for the decision hyperplane of linear and nonlinear Support Vector Machines (SVMs). For this we introduce the novel concept of $\rho$ and $\eta$ prototypes. The standard formulation for the decision hyperplane is reformulated and expressed in terms of the two prototypes. Different kernels are treated separately to achieve further classification efficiency and to facilitate its adaptation to operate with the fast Fourier transform to achieve fast eye detection. Using the eye locations, we extract and normalise the face for size and in-plane rotations. Our method produces a more efficient representation of the SVM decision hyperplane than the well-known reduced set methods. As a result, our eye detection subsystem is faster and more accurate. The use of fractals and fractal image coding for object recognition has been proposed and used by others. Fractal codes have been used as features for recognition, but we need to take into account the distance between codes, and to ensure the continuity of the parameters of the code. We use a method based on fractal image coding for recognition, which we call the Fractal Neighbour Distance (FND). The FND relies on the Euclidean metric and the uniqueness of the attractor of a fractal code. An advantage of using the FND over fractal codes as features is that we do not have to worry about the uniqueness of, and distance between, codes. We only require the uniqueness of the attractor, which is already an implied property of a properly generated fractal code. Similar methods to the FND have been proposed by others, but what distinguishes our work from the rest is that we investigate the FND in greater detail and use our findings to improve the recognition rate. Our investigations reveal that the FND has some inherent invariance to translation, scale, rotation and changes to illumination. These invariances are image dependent and are affected by fractal encoding parameters. The parameters that have the greatest effect on recognition accuracy are the contrast scaling factor, luminance shift factor and the type of range block partitioning. The contrast scaling factor affect the convergence and eventual convergence rate of a fractal decoding process. We propose a novel method of controlling the convergence rate by altering the contrast scaling factor in a controlled manner, which has not been possible before. This helped us improve the recognition rate because under certain conditions better results are achievable from using a slower rate of convergence. We also investigate the effects of varying the luminance shift factor, and examine three different types of range block partitioning schemes. They are Quad-tree, HV and uniform partitioning. We performed experiments using various face datasets, and the results show that our method indeed performs better than many accepted methods such as eigenfaces. The experiments also show that the FND based classifier increases the separation between classes. The standard FND is further improved by incorporating the use of localised weights. A local search algorithm is introduced to find a best matching local feature using this locally weighted FND. The scores from a set of these locally weighted FND operations are then combined to obtain a global score, which is used as a measure of the similarity between two face images. Each local FND operation possesses the distortion invariant properties described above. Combined with the search procedure, the method has the potential to be invariant to a larger class of non-linear distortions. We also present a set of locally weighted FNDs that concentrate around the upper part of the face encompassing the eyes and nose. This design was motivated by the fact that the region around the eyes has more information for discrimination. Better performance is achieved by using different sets of weights for identification and verification. For facial verification, performance is further improved by using normalised scores and client specific thresholding. In this case, our results are competitive with current state-of-the-art methods, and in some cases outperform all those to which they were compared. For facial identification, under some conditions the weighted FND performs better than the standard FND. However, the weighted FND still has its short comings when some datasets are used, where its performance is not much better than the standard FND. To alleviate this problem we introduce a voting scheme that operates with normalised versions of the weighted FND. Although there are no improvements at lower matching ranks using this method, there are significant improvements for larger matching ranks. Our methods offer advantages over some well-accepted approaches such as eigenfaces, neural networks and those that use statistical learning theory. Some of the advantages are: new faces can be enrolled without re-training involving the whole database; faces can be removed from the database without the need for re-training; there are inherent invariances to face distortions; it is relatively simple to implement; and it is not model-based so there are no model parameters that need to be tweaked.
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Li, Jiawei. "Person re-identification with limited labeled training data." HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/541.
Full textAbstract:
With the growing installation of surveillance video cameras in both private and public areas, it is an immediate requirement to develop intelligent video analysis system for the large-scale camera network. As a prerequisite step of person tracking and person retrieval in intelligent video analysis, person re-identification, which targets in matching person images across camera views is an important topic in computer vision community and has been received increasing attention in the recent years. In the supervised learning methods, the person re-identification task is formulated as a classification problem to extract matched person images/videos (positives) from unmatched person images/videos (negatives). Although the state-of-the-art supervised classification models could achieve encouraging re-identification performance, the assumption that label information is available for all the cameras, is impractical in large-scale camera network. That is because collecting the label information of every training subject from every camera in the large-scale network can be extremely time-consuming and expensive. While the unsupervised learning methods are flexible, their performance is typically weaker than the supervised ones. Though sufficient labels of the training subjects are not available from all the camera views, it is still reasonable to collect sufficient labels from a pair of camera views in the camera network or a few labeled data from each camera pair. Along this direction, we address two scenarios of person re-identification in large-scale camera network in this thesis, i.e. unsupervised domain adaptation and semi-supervised learning and proposed three methods to learn discriminative model using all available label information and domain knowledge in person re-identification. In the unsupervised domain adaptation scenario, we consider data with sufficient labels as the source domain, while data from the camera pair missing label information as the target domain. A novel domain adaptive approach is proposed to estimate the target label information and incorporate the labeled data from source domain with the estimated target label information for discriminative learning. Since the discriminative constraint of Support Vector Machines (SVM) can be relaxed into a necessary condition, which only relies on the mean of positive pairs (positive mean), a suboptimal classification model learning without target positive data can be those using target positive mean. A reliable positive mean estimation is given by using both the labeled data from the source domain and potential positive data selected from the unlabeled data in the target domain. An Adaptive Ranking Support Vector Machines (AdaRSVM) method is also proposed to improve the discriminability of the suboptimal mean based SVM model using source labeled data. Experimental results demonstrate the effectiveness of the proposed method. Different from the AdaRSVM method that using source labeled data, we can also improve the above mean based method by adapting it onto target unlabeled data. In more general situation, we improve a pre-learned classifier by adapting it onto target unlabeled data, where the pre-learned classifier can be domain adaptive or learned from only source labeled data. Since it is difficult to estimate positives from the imbalanced target unlabeled data, we propose to alternatively estimate positive neighbors which refer to data close to any true target positive. An optimization problem for positive neighbor estimation from unlabeled data is derived and solved by aligning the cross-person score distributions together with optimizing for multiple graphs based label propagation. To utilize the positive neighbors to learn discriminative classification model, a reliable multiple region metric learning method is proposed to learn a target adaptive metric using regularized affine hulls of positive neighbors as positive regions. Experimental results demonstrate the effectiveness of the proposed method. In the semi-supervised learning scenario, we propose a discriminative feature learning using all available information from the surveillance videos. To enrich the labeled data from target camera pair, image sequences (videos) of the tagged persons are collected from the surveillance videos by human tracking. To extract the discriminative and adaptable video feature representation, we propose to model the intra-view variations by a video variation dictionary and a video level adaptable feature by multiple sources domain adaptation and an adaptability-discriminability fusion. First, a novel video variation dictionary learning is proposed to model the large intra-view variations and solved as a constrained sparse dictionary learning problem. Second, a frame level adaptable feature is generated by multiple sources domain adaptation using the variation modeling. By mining the discriminative information of the frames from the reconstruction error of the variation dictionary, an adaptability-discriminability (AD) fusion is proposed to generate the video level adaptable feature. Experimental results demonstrate the effectiveness of the proposed method.
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Baldasso, Rosane Pérez. "Emprego da fotoantropometria para compreensão do perfil de envelhecimento facial." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/23/23153/tde-06032017-134731/.
Full textAbstract:
Muitas são as tentativas de projetar a aparência de uma pessoa através do tempo. Um recurso muito utilizado para este fim é a simulação de progressão de idade, que consiste na modificação de uma fotografia de uma pessoa, representando o efeito do envelhecimento sobre a sua aparência por meio de processamento de imagem digital ou de desenhos artísticos. Porém, as metodologias utilizadas usualmente são subjetivas. A inexistência de uma metodologia cientificamente consolidada capaz de simular mudanças ocorridas na face, por meio de imagens, evidencia a necessidade de estudos na área, visando subsidiar o perito criminal para a execução da técnica e sua aplicação como auxiliar nos exames de Identificação Facial Forense (IFF). A principal premissa envolvida em qualquer método de Identificação Humana é a da \"unicidade\" da informação utilizada como meio de comparação. Por unicidade, entende-se a individualidade (ou não repetitividade) do parâmetro estudado, que deve ser mensurada com a realização de estudos populacionais específicos, sendo que algumas características faciais se mantêm ao longo dos anos. O presente trabalho teve como objetivo avaliar modificações no padrão métrico da face do indivíduo adulto em função da idade, em específico no que diz respeito ao crescimento das orelhas, crescimento de nariz e alteração da espessura dos lábios, visando a compreensão do processo de envelhecimento e o desenvolvimento de metodologias para estimativas de progressão de idade em casos de desaparecidos e foragidos, dando subsídios ao perito criminal para execução da técnica de simulação de envelhecimento da face em imagens. Para tanto, foram utilizadas imagens faciais padronizadas de 700 indivíduos adultos brasileiros, do sexo feminino e do sexo masculino, da região Sul, ancestralidade branca e em idades de 20, 30, 40, 50, 60, 70 e 80 anos, todas em norma frontal oriundas do Sistema Nacional de Passaporte (SINPA), gerenciado pela Polícia Federal. Os resultados apontaram um aumento gradual e mensurável da altura das orelhas, intimamente relacionado com o aumento dos lóbulos, aumento da largura do nariz, bem como a diminuição da porção mucosa dos lábios, levemente mais evidente no lábio inferior, sendo estas alterações progressivas ao longo dos grupos etários estudados e diferente entre os sexos. Assim, concluiu-se ser possível determinar metricamente as modificações que ocorrem na face do indivíduo adulto em função da idade, em específico no que diz respeito a alterações de nariz, lábios e orelhas, visando a compreensão do processo de envelhecimento e fornecendo subsídios para execução da técnica de simulação de progressão de idade da face em imagens.There are many attempts to design the look of a person over time. A widely used resource for this purpose is the simulation of age progression, which is the modification of a person\'s photograph, representing the effect of aging on their appearance through digital image processing or artistic designs. However, the methodologies commonly used are subjective. The absence of a scientifically consolidated methodology capable of simulating changes in the face, through images, highlights the need for studies in the area, in order to support the criminal expert for technical execution, for their application to assist in the Forensic Facial Identification (FFI). The main premise involved in any method of human identification is the \"uniqueness\" of information used as a comparison. For uniqueness means individuality (or non-repeatability) of the studied parameter, which should be measured to perform specific population studies, with some facial features that are maintained over the years. This study evaluated changes in the metric pattern of the face of adult people according to age, in particular with regard to the growth of ears, nose and change in the thickness of the lips, aiming to understand the aging process and the development of methodologies for age progression to estimate in cases of missing persons and fugitives, giving subsidies to perform the face of the aging simulation technique images. Therefore, were used facial images of 700 South Brazilian adults of both sexes, white ancestry and 20 30, 40, 50, 60, 70 and 80 ages, all in the frontal picture, coming from the National Passport System managed by the Federal Police. Results showed a gradual and measurable increase the height of the ears closely related with the increase of the lobes, increased nose width, as well as decreasing mucous portion of the lips slightly more evident in the lower lip, and these incremental changes over the age groups studied and are different between the sexes. Thus, it was found possible to determine metrically changes that occur in the face of adult individuals depending on age, in particular as regards the nose changes, lips and ears, aiming to understand the aging process and providing data to running the technique of simulating the progression of age in face images.
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Bevans, Rebecca L. "Who knows baby best? investigating connotative gender information, gender processing,and gender identification by adults /." abstract and full text PDF (free order & download UNR users only), 2008. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3339094.
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Ferguson, Eilidh Louise. "Facial identification of children : a test of automated facial recognition and manual facial comparison techniques on juvenile face images." Thesis, University of Dundee, 2015. https://discovery.dundee.ac.uk/en/studentTheses/03679266-9552-45da-9c6d-0f062c4893c8.
Full textAbstract:
The accurate identification of children from facial photographs could provide a great attribute in the fight against child sexual exploitation, and may also aid in the detection of missing juveniles where comparative material is available. The European Commission is actively pursuing a global alliance for the identification of the victims of child sexual abuse; a task which is considered to be of the utmost importance. Images of child sexual abuse are shared, copied, and distributed online and their origin can be difficult to trace. Current investigations attempting to identify the children within such images appear to focus on the determination of places or geographical regions depicted in these images, from which victims can subsequently be tracked down and identified. Cutting edge technology is also used to detect duplicate images in order to decrease the workload of human operators and dedicate more time to the identification of new victims. Present investigations do not appear to focus on facial information for victim identification. Methods of facial identification already exist for adult individuals, consisting of both automated facial recognition algorithms and manual facial comparison techniques carried out by human operators. Human operator image comparison is presently the only method considered accurate enough to verify a face identity. It is only recently that researchers involved in automated facial recognition have begun to concern themselves with identification spanning childhood. Methods focus on age simulation to match query images with the age of the target database, rather than discrimination of individual faces over age progression. As far as can be determined, this is the first attempt to assess the manual comparison of juvenile faces. This study aimed to create a database of children’s faces from which identification accuracy could be tested using both automated facial recognition and manual facial comparison methods, which already exist for the identification of adults. A state-of-the-art facial recognition algorithm was employed and manual facial comparison was based on current recommendations by the Facial Identification Scientific Working Group (FISWG). It was not known if methods based on adult faces could be successfully extrapolated to juvenile faces, particularly as facial identification is highly susceptible to errors when there is an age difference between images of an individual. In children, the face changes much more rapidly than adults over ageing, due to the rapid growth and development of the juvenile face. The results of this study are in agreement with comparisons of automated and human performance in the identification of adult faces. Overall the automated facial recognition algorithm superseded human ability for identification of juvenile faces, however human performance was higher for the most difficult face pairs. The average accuracy for human image comparison was 61%. There was no significant difference in juvenile identification between individuals with prior experience of adult facial comparison and those with no prior experience. For automated facial recognition a correct identification rate of 71% was achieved at a false acceptance rate of 9%. Despite using methods created for adult facial identification, the results of this study are promising, particularly as they are based on a set of images acquired under uncontrolled conditions, which is known to increase error rates. With further augmentation of the database and investigation into child-specific identification techniques, the ability to accurately identify children from facial images is certainly a future possibility.
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Chuang, Xin Sulynn. "“My face! give it back!” : interrogating mask metaphors and identification in Scott McCloud's Understanding Comics." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/35009.
Full textAbstract:
This thesis argues that one way to resolve some of the discrepancies in the theory of
identification proposed by Scott McCloud in Understanding Comics, such as his mask metaphor, is to approach his theory via theatrical conceits. By thinking of identification in the terms of an actor playing a masked character, in which to read a comic and identify with a cartoon character means to put on a mask and imaginatively play the character, McCloud’s contention of cartoons matching our basic mind-pictures becomes readily resolved by virtue of the fact that the mask is serving as a dramatic signifier of the reader’s inner reality. That is, by imaginatively bringing to life the iconic cartoon form, the reader mimetically becomes the character, hence making it entirely plausible for anyone to enter the world of the cartoon and see themselves in the faces of the characters. The mask thus becomes a logo that transforms the reader’s body into logos, granting access to the realm of the symbolic by covering up a reader’s personal identity such that he or she becomes a cipher, at liberty to see whatever he or she wants in the cartoon image. However, regarding the comics panel as a kind of dramatic stage in which the identifying reader is intimately involved as both actor and initiator of theatrical communication, raises other problems. It not only problematises the distinction between reality and artifice in an imaginative performance context, but also ignores the fact that masks are frequently used for purposes of preventing rather than promoting audience identification. McCloud’s theory, in attempting to circumvent the issues surrounding the fraught relationship between self and other that are inherent in any discussion of identification by applying the mask as a structuring term, raises new issues of its own.
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Karhánek, Martin. "Identifikace obličeje na platformě Android." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-237046.
Full textAbstract:
This work describes ways to use a person identification based on faces on mobile devices with Android platform. A reader is introduced into a structure of this system and a way to create applications for it. Besides, there are also methods usable to the face identification. Some of these methods (used in an implementation) are described in more detail. This work also contains a description of model AAM (Active Appearance Model) for implementing in mobile devices and evaluation of used algorithms results.