Статті в журналах: "Face Analysi"

1

Salmela, Viljami, Ilkka Muukkonen, Jussi Numminen, and Kaisu Ölander. "Spatiotemporal dynamics of face processing network studied with combined multivariate EEG and fMRI analysi." Journal of Vision 17, no. 10 (August 31, 2017): 1263. http://dx.doi.org/10.1167/17.10.1263.

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Muqorobin, Muqorobin, and Nendy Akbar Rozaq Rais. "Analysis of the Role of Information Systems Technology in Lecture Learning during the Corona Virus Pandemic." International Journal of Computer and Information System (IJCIS) 1, no. 1 (August 27, 2020): 47–51. http://dx.doi.org/10.29040/ijcis.v1i2.15.

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Abstract— At this time the spread of the Corona Covid-19 Virus was sweeping the world, Indonesia was also affected, especially in the world of education where the teaching and learning process was usually carried out face-to-face in the classroom. So as a result of this pendemi the teaching and learning process must be done online. The role of information systems technology is very meaningful in lecture learning. This study aims to analyze a model of campus learning conditions and the role of information system technology in college learning amid the COVID-19 corona virus pandemic at STMIK Sinar Nusantara Surakarta. The research method is to make observations and literature studies to obtain data and information used in research. The results of this study indicate the use of information technology has a very important role in the implementation of online distance learning in the midst of the corona covid 19 virus pandemic, among online online media such as: google classroom, whatsapp, zoom. Of the online learning media, it is proven that Google Classroom: 55.9% is widely used as media for sharing materials and assignments, while video conferences lectures are the most users of Google Meet as much as: 70.6%. The results of the analysi s of the online learning value are: 44.1%. Based on this data, it shows that the role of information system technology plays an important role and helps in the teaching and learning process amid the Covid-19 corona virus pandemic.

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3

Wiskott, Laurenz. "Phantom faces for face analysis." Pattern Recognition 30, no. 6 (June 1997): 837–46. http://dx.doi.org/10.1016/s0031-3203(96)00132-x.

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4

Michele de Oliveira, Sandi, and Nieves Hernández-Flores. "Desafíos interpretativos en el análisis de la imagen sociocultural." Textos en Proceso 1, no. 1 (December 1, 2015): 1–15. http://dx.doi.org/10.17710/tep.2015.1.1.1oli.

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5

Koleva, Emiliya, and Neli Baeva. "A Comparative Analysis of Assessment Results From Face-To-Face and Online Exams." Mathematics and Informatics LXV, no. 4 (August 30, 2022): 335–43. http://dx.doi.org/10.53656/math2022-4-1-aco.

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In this study, a comparative analysis of the results of students’ performance on a face-to-face and an online exam is made and presented. The students involved in the research are trained and evaluated by the same examinator. Different statistical tests are made using statistical analysis software. As a result of the research, the hypothesis is confirmed that there is a difference between the two evaluations. Comparison of the grades between the different exams showed that there is a linear relationship between them, there is dependence between the results from both exams and the results from the online exam are slightly higher than the results from the face-to-face exam.

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Nikolaievskyi, O. Yu, O. V. Skliarenko, and A. I. Sidorchuk. "ANALYSIS AND COMPARISON OF FACE DETECTION APIS." Telecommunication and information technologies, no. 4 (2019): 39–45. http://dx.doi.org/10.31673/2412-4338.2019.043945.

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7

Phillips, Ian. "Object files and unconscious perception: a reply to Quilty-Dunn." Analysis 80, no. 2 (November 9, 2019): 293–301. http://dx.doi.org/10.1093/analys/anz046.

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Abstract A wealth of cases – most notably blindsight and priming under inattention or suppression – have convinced philosophers and scientists alike that perception occurs outside awareness. In recent work (Phillips 2016a, 2018; Phillips and Block 2017, Peters et al. 2017), I dispute this consensus, arguing that any putative case of unconscious perception faces a dilemma. The dilemma divides over how absence of awareness is established. If subjective reports are used, we face the problem of the criterion: the concern that such reports underestimate conscious experience (Eriksen 1960, Holender 1986, Peters and Lau 2015). If objective measures are used, we face the problem of attribution: the concern that the case does not involve genuine individual-level perception. Quilty-Dunn (2019) presents an apparently compelling example of unconscious perception due to Mitroff et al. (2005) which, he contends, evades this dilemma. The case is fascinating. However, as I here argue, it does not escape the dilemma’s clutches.

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8

Scharp, Kevin. "Shrieking in the face of vengeance." Analysis 78, no. 3 (February 6, 2018): 454–63. http://dx.doi.org/10.1093/analys/anx163.

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9

Moore, A. W. "Not to be Taken at Face Value." Analysis 69, no. 1 (January 1, 2009): 116–25. http://dx.doi.org/10.1093/analys/ann040.

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10

Del Líbano, Mario, Manuel G. Calvo, Andrés Fernández-Martín, and Guillermo Recio. "Discrimination between smiling faces: Human observers vs. automated face analysis." Acta Psychologica 187 (June 2018): 19–29. http://dx.doi.org/10.1016/j.actpsy.2018.04.019.

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11

Jia-Li Li, Jia-Li Li, Xing-Guo Jiang Jia-Li Li, Li He Xing-Guo Jiang, and De-Cai Li Li He. "Face Age Feature Analysis Based on Improved Conditional Adversarial Auto-encoder (I-CAAE)." 電腦學刊 34, no. 1 (February 2023): 063–73. http://dx.doi.org/10.53106/199115992023023401005.

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In recent years, the research of face age features has achieved rapid development driven by deep learning. The faces generated by the Conditional Adversarial Auto-encoder (CAAE) model are not only highly credible, but also closer to the target age. However, there are many problems, such as low resolution of human face image generation and poor local feature retention effect of human face features. To this end, this paper improves on the CAAE network. Firstly, referring to the LSGAN network structure, the 4 convolution layers of the encoder are added to 5 layers and the 4 convolution layers of the generator are added to 7 layers. Secondly, on the basis of the original loss function, the image gradient difference loss function is added to ensure the output face image quality. Meanwhile, the data set were preprocessed for face correction. Finally, this paper performs face similarity analysis on the Eye-key platform and contrasts the generated image quality using structural similarity and peak signal to noise ratio metrics. In addition, the generated results were tested for their robustness. The experimental results show that the average similarity of faces generated by the Improved Conditional Adversarial Auto-encoder (I-CAAE) network was increased by 3.9. And the average peak signal to noise ratio of the generated pictures was reduced by 1.8. Confirming the superiority of the proposed method.

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12

Lee, Jeongeun, Hyeseon Jeong, Minji Kim, Jihyun Kim, and Young Sook Son. "Good Bank Evaluation by Chernoff Face Analysis using SAS macro faces." Korean Journal of Applied Statistics 26, no. 6 (December 31, 2013): 959–75. http://dx.doi.org/10.5351/kjas.2013.26.6.959.

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13

Żochowska, Anna, Maria M. Nowicka, Michał J. Wójcik, and Anna Nowicka. "Self-face and emotional faces—are they alike?" Social Cognitive and Affective Neuroscience 16, no. 6 (February 8, 2021): 593–607. http://dx.doi.org/10.1093/scan/nsab020.

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Abstract The image of one’s own face is a particularly distinctive feature of the self. The self-face differs from other faces not only in respect of its familiarity but also in respect of its subjective emotional significance and saliency. The current study aimed at elucidating similarities/dissimilarities between processing of one’s own face and emotional faces: happy faces (based on the self-positive bias) and fearful faces (because of their high perceptual saliency, a feature shared with self-face). Electroencephalogram data were collected in the group of 30 participants who performed a simple detection task. Event-related potential analyses indicated significantly increased P3 and late positive potential amplitudes to the self-face in comparison to all other faces: fearful, happy and neutral. Permutation tests confirmed the differences between the self-face and all three types of other faces for numerous electrode sites and in broad time windows. Representational similarity analysis, in turn, revealed distinct processing of the self-face and did not provide any evidence in favour of similarities between the self-face and emotional (either negative or positive) faces. These findings strongly suggest that the self-face processing do not resemble those of emotional faces, thus implying that prioritized self-referential processing is driven by the subjective relevance of one’s own face.

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14

Tyagi, Surya Kant. "Face Recognition Using Discrete Cosine Transform and Nearest Neighbor Discriminant Analysis." International Journal of Engineering and Technology 4, no. 3 (2012): 311–14. http://dx.doi.org/10.7763/ijet.2012.v4.372.

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15

Soroka, Vladislav B., Mikhail P. Sainov, and Denis V. Korolev. "Concrete-faced rockfill dams: experience in study of stress-strain state." Vestnik MGSU, no. 2 (February 2019): 207–24. http://dx.doi.org/10.22227/1997-0935.2019.2.207-224.

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Introduction. At present the urgent problem in hydraulic construction is establishing the causes of crack formation in seepage-control reinforced concrete faces at a number of rockfill dams. For solving this problem the studies are conducted of stress-strain state (SSS) of concrete-faced rockfill dams which are fulfilled by different methods. Materials and methods. Gives a review and analysis of the results of studies of stress-strain state of concrete-faced rockfill dams (CFRD) fulfilled by different authors over the last 15 years. The results of analytical, experimental and numerical studies are considered. Descriptions are given of the models used for simulation of non-linear character of rockfill deformation at numerical modeling of dam SSS. Results. Analysis showed that solving the problem of CFRD SSS causes a number of methodological difficulties. At present the only method permitting study of CFRD SSS is numerical modeling. The rest methods do not permit considering the impact of important factors on SSS. Large complications are caused by scarce knowledge of rockfill deformation properties in real dams. Conclusions. It was revealed that at present SSS of reinforced concrete faces has been studied insufficiently. The results of conducted studies do not give full and adequate understanding about operation conditions of reinforced concrete faces. Impact of various factors on the face SSS has not been studied. Besides, there are contradictions in the results of studies obtained by different authors. Differences in the results are based on objective and subjective reasons. A considerable obstruction for numerical studies is complicated modeling of rigid thin-walled reinforced concrete face behavior at large deformations inherent to rockfill. The obtained results of studies often do not permit conducting full analysis of SSS of concrete-faced rockfill dams.

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16

Venkatakrishnan Ragu, D., C. Hariram, N. Anantharaj, and A. Muthulakshmi. "3D Face Recognition with Occlusions Using Fisher Faces Projection." Applied Mechanics and Materials 573 (June 2014): 442–46. http://dx.doi.org/10.4028/www.scientific.net/amm.573.442.

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In recent years, the 3-D face has become biometric modal, for security applications. Dealing with occlusions covering the facial surface is difficult to handle. Occlusion means blocking of face images by objects such as sun glasses, kerchiefs, hands, hair and so on. Occlusions are occurred by facial expressions, poses also. Basically consider two things: i) Occlusion handling for surface registration and ii). Missing data handling for classification. For registration to use an adaptively-selected-model based registration scheme is used. After registering occlusions are detected and removed. In order to handle the missing data we use a masking strategy call masked projection technique called Fisher faces Projection. Registration based on the adaptively selected model together with the masked analysis offer an occlusion robust face recognition system.

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17

Yiu, Sheung. "One face, millions of faces: Computer vision as hyperobject." Philosophy of Photography 12, no. 1 (October 1, 2021): 71–91. http://dx.doi.org/10.1386/pop_00048_1.

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Borrowing Timothy Morton’s notion of hyperobject, this article explores questions of network and scale in generative adversarial networks (GAN) images. In this context, the term network refers to the omnipresence of algorithmic images today and their significant impact on our lives. Such images are massively distributed in time and space beyond any sensible human-scale. Scale, in this context, denotes the relations between different operational layers of algorithmic images, such as the pictorial layer in contrast to the data layer. An algorithmic image is simultaneously a visual image, a symbol, a data point and part of a mass visual milieu. Its meaning is thus polymorphic and can, arguably, never be exhausted. The article explores these terms through analysis of the website www.thispersondoesnotexist.com.

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18

Ayo, Femi Emmanuel, Abiodun Muyideen Mustapha, Joachim Ayodeji Braimah, and Daniel Ayodele Aina. "Geometric Analysis and YOLO Algorithm for Automatic Face Detection System in a Security Setting." Journal of Physics: Conference Series 2199, no. 1 (February 1, 2022): 012010. http://dx.doi.org/10.1088/1742-6596/2199/1/012010.

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Abstract Face detection is a computer technology that determines the location and size of the human face in an arbitrary digital image employed for authentication. Face recognition technology plays a vital role in network security, video compression, content indexing, and retrieval since "humans" are the focal point of many videos. Face recognition-based network access control makes it almost difficult for hackers to obtain a user’s "password" and improves user-friendliness in human-computer interaction. In this paper, an automatic face detection system that accurately detects human faces and ignores any other object that is not a human face using geometric analysis and the you-only-look-once (YOLO) algorithm is introduced. The system is able to predict the ages and genders of the faces detected. It also detects the facial landmarks of the faces and indicates the emotions of the faces detected. A sample of four faces is considered for testing the system; thus, accurately detecting gender and emotion but not age correctly. In all, the evaluation shows about 80% accuracy. With the results got, the system can support security and analytics. It can be used to get analytics in an event with a lot of attendees and can also be used to get a facial mapping of someone involved in a crime scene for security purposes.

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19

Lee, Chang-Young, Soo-Kyung Lee, and In-Tae Ko. "The Analysis of Face-Off in Ice-hockey Games." Korean Journal of Sports Science 26, no. 2 (April 30, 2017): 1181–88. http://dx.doi.org/10.35159/kjss.2017.04.26.2.1181.

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20

Dubey, Meeta, and Prashant Jain. "Face Recognition using PCA and LDA Technique for Noisy Faces." International Journal of Electrical and Electronics Research 1, no. 2 (September 30, 2013): 22–28. http://dx.doi.org/10.37391/ijeer.010201.

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Face recognition is always a popular area of research. There are various techniques used in the face recognition system. Principal component analysis (PCA) and linear discriminate analysis (LDA) techniques are the two most well-known techniques for the face recognition. In this paper, the PCA and LDA technique based face recognition system are described. The performance of this technique is compare in term of PSNR and RMSE for noisy image. The Euclidean distance between feature templates and database futures are used for identifying the face image. There are basically three types of noises present, but in this paper I am going to compare the salt and pepper noise with the Gaussian noise in the detailed and analytical ways. After finding the features of the different noisy images I am going to compare both the PCA and LDA technique for the noisy pictures.

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21

Benini, Sergio, Khalil Khan, Riccardo Leonardi, Massimo Mauro, and Pierangelo Migliorati. "Face analysis through semantic face segmentation." Signal Processing: Image Communication 74 (May 2019): 21–31. http://dx.doi.org/10.1016/j.image.2019.01.005.

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22

Kawamura, Satoru, Masashi Komori, and Yusuke Miyamoto. "Smiling Reduces Masculinity: Principal Component Analysis Applied to Facial Images." Perception 37, no. 11 (January 1, 2008): 1637–48. http://dx.doi.org/10.1068/p5811.

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We examined the effect of facial expression on the assignment of gender to facial images. A computational analysis of the facial images was applied to examine whether physical aspects of the face itself induced this effect. Thirty-six observers rated the degree of masculinity of the faces of 48 men, and the degree of femininity of the faces of 48 women. Half of the faces had a neutral facial expression, and the other half was smiling. Smiling significantly reduced the perceived masculinity of men's faces, especially for male observers, whereas no effect of smiling on femininity ratings was obtained for women's faces. A principal component analysis was conducted on the matrix of pixel luminance values for each facial image × all the images. The third principle component explained a relatively high proportion of the variance of both facial expressions and gender of face. These results suggest that the effect of smiling on the assignment of gender is caused, at least in part, by the physical relationship between facial expression and face gender.

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23

Norouzi Masir, Raziye, Mohammad Ataei, Farhang Sereshki, and Ali Nouri Qarahasanlou. "AVAILABILITY SIMULATION AND ANALYSIS OF ARMORED FACE CONVEYOR MACHINE IN LONGWALL MINING." Rudarsko-geološko-naftni zbornik 36, no. 2 (2021): 69–82. http://dx.doi.org/10.17794/rgn.2021.2.7.

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Since coal mining production systems are very complex, repairing equipment is expensive. If a system failure occurs, it will cause disturbances such as inoperable equipment, reduced operating time, increased production costs, and reduced equipment performance. Therefore, it is necessary to consider the availability of the coal mining industry more than ever. For this purpose, the Armored Face Conveyor (AFC) machine failure data was gathered over a period of 29 months from the Tabas Coal Mine. Descriptive statistics, trends, and serial correlation tests of the data were calculated. Then, the system’s mean and point availability were simulated. Based on the results, the mean availability (all events) and point availability (all events) at 360000 h are 96% and 95%, respectively. The mean time to first failure (MTTFF) of the AFC machine was about 23.61 h. The ReliaSoft Failure Criticality Index, ReliaSoft Downing Event Criticality Index, and ReliaSoft Downtime Criticality Index electrical equipping have the largest effect, whereas the main drive subsystem is the least important. Analysis showed that availability has a direct correlation to activity management and improvements in the quality, efficiency, and the product extraction.

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Caballero Zoreda, L. "Método para el análisis estratigráfico de construcciones históricas o "lectura de paramentos"." Informes de la Construcción 46, no. 435 (February 28, 1995): 37–46. http://dx.doi.org/10.3989/ic.1995.v46.i435.1096.

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25

Bovens, L. "The two faces of akratics anonymous." Analysis 59, no. 4 (October 1, 1999): 230–36. http://dx.doi.org/10.1093/analys/59.4.230.

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Xin, Hengqi, Qinghai Li, Li Liu, Zhijun Liu, and Junmin Hou. "Analysis on the Influence of Fault Protection Coal Pillar Size on Rockburst." Geofluids 2021 (March 15, 2021): 1–11. http://dx.doi.org/10.1155/2021/5563347.

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Compared with other types of rockburst, fault rockburst releases the most energy and brings the hugest damage to the stope. Reasonable fault protection coal pillar can effectively prevent and control the occurrence of fault rockburst. Reasonable fault protection coal pillar (FPCP) can prevent and control the occurrence of fault rockburst effectively. Based on the engineering background of No. 7 mining area in a coal mine, this paper analyzes the reasonable coal pillar size on both sides of normal fault. Combined with the geological conditions in site, through the mechanical analysis of coal pillar stability, it is calculated that the critical FPCP size is 27.9 m for the working face in the upper wall and 39.0 m for the working face in the footwall. Through numerical simulation analysis, it is found that with the critical size of FPCP, the stress concentration coefficient in front of the upper wall working faces and footwall working faces is about 1.59. When the size of FPCP is smaller than the critical one, the difference of stress concentration coefficient between the two working faces (upper wall working face and footwall working face) is large, and the difference becomes larger and larger with the decrease of coal pillar size. When the size of FPCP is larger than the critical one, the difference of stress concentration coefficient between the two working faces (upper wall working face and footwall working face) is small, and the stress concentration coefficient of the two faces tends to be equal with the increase of coal pillar size. The rationality of coal pillar size is verified by field application, which provides a basis for the selection of FPCP in subsequent mining under similar conditions.

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Faridi, Muhammad Shakeel, Muhammad Azam Zia, Zahid Javed, Imran Mumtaz, and Saqib Ali. "A Comparative Analysis Using Different Machine Learning: An Efficient Approach for Measuring Accuracy of Face Recognition." International Journal of Machine Learning and Computing 11, no. 2 (March 2021): 115–20. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1023.

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Feature extracting and training module can be done by using face recognition neural learning techniques. Moreover, these techniques are widely employed to extract features from human images. Some detection systems are capable to scan the full body, iris detection, and finger print detection systems. These systems have deployed for safety and security intension. In this research work, we compare different machine learning algorithms for face recognition. Four supervised face recognition machine-learning classifiers such as Principal Component Analysis (PCA), 1-nearest neighbor (1-NN), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are considered. The efficiency of multiple classification systems is also demonstrated and tested in terms of their ability to identify a face correctly. Face Recognition is a technique to identify faces of people whose images are stored in some databases and available in the form of datasets. Extensive experiments conducted on these datasets. The comparative analysis clearly shows that which machine-learning algorithm is the best in terms of accuracy of image detection. Despite the fact, other identification methods are also very effective; face recognition has remained a major focus of research due to its non-meddling nature and being the easy method of personal identification for people. The findings of this work would be useful identification of a suitable machine-learning algorithm in order to achieve better face recognition accuracy.

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Zarei, Shima. "Face recognition methods analysis." International Journal Artificial Intelligent and Informatics 1, no. 1 (July 10, 2018): 01. http://dx.doi.org/10.33292/ijarlit.v1i1.13.

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Face Recognition is one of the most important issues in Image processing tasks. It is important because it uses for various purposes in real world such as Criminal detection or for detecting fraud in passport and visa check in airports. Face book is a nice example of Face recognition application, when it sends notification to one user’s friends who are recognized by their images that user uploaded in face book page. To solve Face Recognition problem different methods are introduced such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Hidden Markov Models (HMM) which are explained and analyzed. Also algorithms like; Eigen face, Fisher face and Local Binary Pattern Histogram (LBPH) which are simplest and most accurate methods are implemented in this project for AT&T dataset to recognize the most similar face to other faces in this data set. To this end these algorithms are explained and advantages and disadvantages of each one are analyzed as well. Consequently, the best method is selected with comparison between the results of face reconstruction by Engine face, Fisher face and Local binary pattern histogram methods. In this project Eigen face method has best result. It should be noted that for implementing face recognition algorithms color map methods are used to distinguish the facial features more precisely. In this work Rainbow color map in Eigen Face algorithm and HSV color map in Fisher Face algorithm are utilized and results shows that HSV color map is more accurate than rainbow color map.

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Sultanov, Zh. "ANALYSIS OF METHODS FOR DETECTING FACES IN AN IMAGE." Scientific Journal of Astana IT University, no. 7 (October 30, 2021): 77–88. http://dx.doi.org/10.37943/aitu.2021.48.48.007.

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In this article, computer vision is considered as modern technology of automatic processing of graphic images, and the relationship between the terms “computer vision” and “machine vision” is investigated. A diagram of a typical computer vision system is given and the possibility of using a system based on an artificial neural network for image analysis is considered. The article analyses the current situation with the use of computer vision systems and the possibility of its application. This article presents face recognition algorithms for existing categories, including: empirical method; feature method – invariant feature; use the template specified by the developer for identification; study the method of detecting the system by external signs. The empirical method of “top-down knowledge-based methods” involves creating an algorithm that implements a set of rules that image segments must satisfy in order to be recognized as faces. Feature-invariant approaches (Feature-invariant approaches) based on bottom-up knowledge constitute the second group of face detection methods. The methods of this group have the ability to recognize faces in different places as an advantage. Use the template set by the developer for identification (template matching method). Templates define specific standard images of face images, for example, describing the attributes of different areas of the face and their possible mutual positions. A method for detecting faces by external signs (a method for performing the training stage of the system by processing test images). The image (or its fragments) is somehow assigned a calculated feature vector, which is used to classify the image into two categories – human face/non-human face.

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Shtewi, Amina R. "Study of Principal Component Analysis (PCA) as a Face Recognition Method." Scientific Journal for Faculty of Science-Sirte University 2, no. 1 (April 17, 2022): 28–32. http://dx.doi.org/10.37375/sjfssu.v2i1.218.

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Анотація:

Face recognition is a biometric technique that can be used for a variety of purposes, such as national security, access control, identity fraud, banking, and finding missing children. Faces are highly dynamic and facial features are not always easily extracted, which can lead to discarding textural information like the smoothness of faces, a hairstyle that, might contain strong identity information. In addition, brightness, scale, and facial expressions play a significant role in the face-recognizing process. Therefore, face recognition is considered as a difficult problem. To figure out this problem effective methods using databases techniques are needed. This paper describes face recognition methods and their structure. Based on Wen Yi Zhao and Rama Chellappa work the face recognition methods are divided into three groups: a holistic approach, feature-based approach, and hybrid approach, where Principal Component Analysis PCA, a holistic approach method, is presented as a mathematical technique that can assist the process of face recognition. Also, the paper shows how the PCA is used to extract facial features by removing the principal components of the available multidimensional data.

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Tamimi, Abdelfatah Aref, Omaima Nazar Al-Allaf, and Mohammad Ahmad Alia. "Eigen Faces and Principle Component Analysis for Face Recognition Systems: A Comparative Study." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 4 (February 28, 2015): 5650–60. http://dx.doi.org/10.24297/ijct.v14i4.1967.

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Анотація:

Face recognition has been largely used in biometric field as a security measure at air ports, passport verification, criminals' list verification, visa processing, and so on. Various literature studies suggested different approaches for face recognition systems and most of these studies have limitations with low performance rates. Eigenfaces and principle component analysis (PCA) can be considered as most important face recognition approaches in the literature. There is a need to develop algorithms and approaches that overcome these disadvantages and improve performance of face recognition systems. At the same time, there is a lack of literature studies which are related to face recognition systems based on EigenFaces and PCA. Therefore, this work includes a comparative study of literature researches related to Eigenfaces and PCA for face recognition systems. The main steps, strengths and limitations of each study will be discussed. Many recommendations were suggested in this study.

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Smith, Lauren Reichart, and Skye C. Cooley. "International Faces: An Analysis of Self-Inflicted Face-ism in Online Profile Pictures." Journal of Intercultural Communication Research 41, no. 3 (November 2012): 279–96. http://dx.doi.org/10.1080/17475759.2012.728771.

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33

Mesman, Judi, Marinus H. van IJzendoorn, and Marian J. Bakermans-Kranenburg. "The many faces of the Still-Face Paradigm: A review and meta-analysis." Developmental Review 29, no. 2 (June 2009): 120–62. http://dx.doi.org/10.1016/j.dr.2009.02.001.

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34

Barat, Elodie, Sylvia Wirth, and Jean-René Duhamel. "Face cells in orbitofrontal cortex represent social categories." Proceedings of the National Academy of Sciences 115, no. 47 (November 5, 2018): E11158—E11167. http://dx.doi.org/10.1073/pnas.1806165115.

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Perceiving social and emotional information from faces is a critical primate skill. For this purpose, primates evolved dedicated cortical architecture, especially in occipitotemporal areas, utilizing face-selective cells. Less understood face-selective neurons are present in the orbitofrontal cortex (OFC) and are our object of study. We examined 179 face-selective cells in the lateral sulcus of the OFC by characterizing their responses to a rich set of photographs of conspecific faces varying in age, gender, and facial expression. Principal component analysis and unsupervised cluster analysis of stimulus space both revealed that face cells encode face dimensions for social categories and emotions. Categories represented strongly were facial expressions (grin and threat versus lip smack), juvenile, and female monkeys. Cluster analyses of a control population of nearby cells lacking face selectivity did not categorize face stimuli in a meaningful way, suggesting that only face-selective cells directly support face categorization in OFC. Time course analyses of face cell activity from stimulus onset showed that faces were discriminated from nonfaces early, followed by within-face categorization for social and emotion content (i.e., young and facial expression). Face cells revealed no response to acoustic stimuli such as vocalizations and were poorly modulated by vocalizations added to faces. Neuronal responses remained stable when paired with positive or negative reinforcement, implying that face cells encode social information but not learned reward value associated to faces. Overall, our results shed light on a substantial role of the OFC in the characterizations of facial information bearing on social and emotional behavior.

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CHEN, SHAOKANG, BRIAN C. LOVELL, and TING SHAN. "ROBUST ADAPTED PRINCIPAL COMPONENT ANALYSIS FOR FACE RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 03 (May 2009): 491–520. http://dx.doi.org/10.1142/s0218001409007284.

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Recognizing faces with uncontrolled pose, illumination, and expression is a challenging task due to the fact that features insensitive to one variation may be highly sensitive to the other variations. Existing techniques dealing with just one of these variations are very often unable to cope with the other variations. The problem is even more difficult in applications where only one gallery image per person is available. In this paper, we describe a recognition method, Adapted Principal Component Analysis (APCA), that can simultaneously deal with large variations in both illumination and facial expression using only a single gallery image per person. We have now extended this method to handle head pose variations in two steps. The first step is to apply an Active Appearance Model (AAM) to the non-frontal face image to construct a synthesized frontal face image. The second is to use APCA for classification robust to lighting and pose. The proposed technique is evaluated on three public face databases — Asian Face, Yale Face, and FERET Database — with images under different lighting conditions, facial expressions, and head poses. Experimental results show that our method performs much better than other recognition methods including PCA, FLD, PRM and LTP. More specifically, we show that by using AAM for frontal face synthesis from high pose angle faces, the recognition rate of our APCA method increases by up to a factor of 4.

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Al-Sultany, Ghaidaa A., and Saba Mohammed Hussain. "Fake Reviews Detection through Users Behavior Analysis." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (October 31, 2019): 737–41. http://dx.doi.org/10.5373/jardcs/v11sp10/20192864.

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37

Padhy, Rajalaxmi, Aishwarya Dash, Sanjit Kumar Dash, and Jibitesh Mishra. "Improved Face Recognition With Fractal-Based Texture Analysis." International Journal of Computer Vision and Image Processing 11, no. 3 (July 2021): 41–53. http://dx.doi.org/10.4018/ijcvip.2021070103.

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Fractals are useful to uniquely represent texture in the human face, which serves as an equivalent of human vision. FaceNet, calculating face descriptors of a person, has been observed to perform with setbacks when several factors of occlusion are present. This paper proposes a new methodology that exploits the self-similar patterns in a person's face to highlight and enhance regions of high texture in a facial image. The system maps the original image into a representation in the pre-processing stage of computer vision. This representation when fed as an input to the FaceNet CNN optimizes the face embedding generated. An SVM classifier separates the hard positive examples from the hard negative examples during classification. The model is trained using YouTube Faces DB as primary dataset and for validation; a custom dataset is designed to verify a person's identity despite the presence of secondary factors such as expressions and forgery. The proposed model attained an overall accuracy of 96.73% with the YouTube Faces DB, and a notable reduction in the false positive rates is observed.

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Singh, Avinash Kumar, Piyush Joshi, and G. C. Nandi. "Face liveness detection through face structure analysis." International Journal of Applied Pattern Recognition 1, no. 4 (2014): 338. http://dx.doi.org/10.1504/ijapr.2014.068327.

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Adji, Sandra Sukmaning, and Suciati Suciati. "Face-to-face Tutorial : Students’ Satisfaction Analysis." International Journal of Pedagogy and Teacher Education 3, no. 1 (September 13, 2019): 64. http://dx.doi.org/10.20961/ijpte.v3i1.34535.

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One of the characteristics of distance learning students study away from educational organization, and Institution should provide learning assistance to its students in academic services which is implemented with tutor guidance. The students will feel satisfied when their learning had fulfilled their needs. This study aims to analyse the students’ level of satisfaction and expectations in the tutorial activities of some subjects provided in Post Graduate Program. Indicator used in achieving the satisfaction are the passage of communication among students, the interaction in the class face to face tutorial and the ability of tutors in guiding student learning. The data obtained through questionnaires to students from 10 region. The sample of this study were 260 from 1198 students, and populations come from 3 magister program. 30 students use as pilot project through 18 items questionnaire test. By using "product moment" correlation technique, obtained each items pertained valid. The result showed in average, the students gave satisfaction assessment above 3.25 of the scale 4. The items were students’ satisfaction on the tutorial environments; students’ satisfaction on the communication intertwined with the face-to-face tutors; students’ satisfaction on the tutors’ capability in transferring the science substance; and satisfaction to the interaction occurred in face-to-face tutorials and the tutor’s role in tutorial activities. There was a relationship between students’ satisfaction in following face-to-face tutorial with the final semester exam, Master of Basic Education R2 = 0,027, Master of Public Administration R2 = 0,01 and Master of Management equal to R2 = 0,015.

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40

Immerman, Daniel. "Knowledge-to-fact arguments can deliver knowledge." Analysis 78, no. 1 (December 8, 2017): 52–56. http://dx.doi.org/10.1093/analys/anx152.

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Naufal, Mohammad Farid, Selvia Ferdiana Kusuma, Zefanya Ardya Prayuska, Ang Alexander Yoshua, Yohanes Albert Lauwoto, Nicky Setyawan Dinata, and David Sugiarto. "Comparative Analysis of Image Classification Algorithms for Face Mask Detection." Journal of Information Systems Engineering and Business Intelligence 7, no. 1 (April 27, 2021): 56. http://dx.doi.org/10.20473/jisebi.7.1.56-66.

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Background: The COVID-19 pandemic remains a problem in 2021. Health protocols are needed to prevent the spread, including wearing a face mask. Enforcing people to wear face masks is tiring. AI can be used to classify images for face mask detection. There are a lot of image classification algorithm for face mask detection, but there are still no studies that compare their performance.Objective: This study aims to compare the classification algorithms of classical machine learning. They are k-nearest neighbors (KNN), support vector machine (SVM), and a widely used deep learning algorithm for image classification which is convolutional neural network (CNN) for face masks detection.Methods: This study uses 5 and 3 cross-validation for assessing the performance of KNN, SVM, and CNN in face mask detection.Results: CNN has the best average performance with the accuracy of 0.9683 and average execution time of 2,507.802 seconds for classifying 3,725 faces with mask and 3,828 faces without mask images.Conclusion: For a large amount of image data, KNN and SVM can be used as temporary algorithms in face mask detection due to their faster execution times. At the same time, CNN can be trained to form a classification model. In this case, it is advisable to use CNN for classification because it has better performance than KNN and SVM. In the future, the classification model can be implemented for automatic alert system to detect and warn people who are not wearing face masks.

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Manjula, V. S. "ANALYSIS OF HUMAN FACE RECOGNITION ALGORITHM USING PCA+FDIT IN IMAGE DATABASE FOR CRIME INVESTIGATION." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 3 (April 30, 2013): 788–96. http://dx.doi.org/10.24297/ijct.v4i3.4201.

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In general, the field of face recognition has lots of research that have put interest in order to detect the face and to identify it and also to track it. Many researchers have concentrated on the face identification and detection problem by using various approaches. The proposed approach is further very useful and helpful in real time application. Thus the Face Detection, Identification Â which is proposed here is used to detect the faces in videos in the real time application by using the FDIT (Face Detection Identification Technique) algorithm. Thus the proposed mechanism is very help full in identifying individual persons who are been involved in the action of robbery, murder cases and terror activities. Although in face recognition the algorithm used is of histogram equalization combined with Back propagation neural network in which we recognize an unknown test image by comparing it with the known training set images that are been stored in the database. Also the proposed approach uses skin color extraction as a parameter for face detection. A multi linear training and rectangular face feature extraction are done for training, identifying and detecting. Â Â Thus the proposed technique Â Â is PCA + FDIT technique configuration only improved recognition for subjects in images are included in the training data.Â Â It is very useful in identify a single person from a group of faces. Â Â Thus the proposed technique is well suited for all kinds faces frame work for face detection and identification. The face detection and identification modules share the same hierarchical architecture. They both consist of two layers of classifiers, a layer with a set of component classifiers and a layer with a single combination classifier. Â Also we have taken a real life example and simulated the algorithms in IDL Tool successfully.

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Bentin, Shlomo, Yulia Golland, Anastasia Flevaris, Lynn C. Robertson, and Morris Moscovitch. "Processing the Trees and the Forest during Initial Stages of Face Perception: Electrophysiological Evidence." Journal of Cognitive Neuroscience 18, no. 8 (August 1, 2006): 1406–21. http://dx.doi.org/10.1162/jocn.2006.18.8.1406.

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Although configural processing is considered a hallmark of normal face perception in humans, there is ample evidence that processing face components also contributes to face recognition and identification. Indeed, most contemporary models posit a dual-code view in which face identification relies on the analysis of individual face components as well as the spatial relations between them. We explored the interplay between processing face configurations and inner face components by recording the N170, an event-related potential component that manifests early detection of faces. In contrast to a robust N170 effect elicited by line-drawn schematic faces compared to line-drawn schematic objects, no N170 effect was found if a pair of small objects substituted for the eyes in schematic faces. However, if a pair of two miniaturized faces substituted for the eyes, the N170 effect was restored. Additional experiments ruled out an explanation on the basis of miniaturized faces attracting attention independent of their location in a face-like configuration and show that global and local face characteristics compete for processing resources when in conflict. The results are discussed as they relate to normal and abnormal face processing.

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44

HSIEH, JUN-WEI, and YEA-SHUAN HUANG. "MULTIPLE-PERSON TRACKING SYSTEM FOR CONTENT ANALYSIS." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 04 (June 2002): 447–62. http://dx.doi.org/10.1142/s0218001402001800.

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This paper presents a framework to track multiple persons in real-time. First, a method with real-time and adaptable capability is proposed to extract face-like regions based on skin, motion and silhouette features. Then, an adaptable skin model is used for each detected face to overcome the changes of the observed environment. After that, a two-stage face verification algorithm is proposed to quickly eliminate false faces based on face geometries and the SVM (Support Vector Machine) approach. In order to overcome the effect of lighting changes, during verification, a method of color constancy compensation is proposed. Then, a robust tracking scheme is applied to identify multiple persons based on a face-status table. With the table, the proposed system has powerful capabilities to track different persons at different statuses, which is quite important in face-related applications. Experimental results show that the proposed method is more robust and powerful than other traditional methods, which utilize only color, motion information, and the correlation technique.

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Aziz, Azim Zaliha Abd, and Mohd Rizon Mohamed Juhari. "Face spoofing detection using surface and sub-surface reflections analysis." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (October 1, 2021): 189. http://dx.doi.org/10.11591/ijeecs.v24.i1.pp189-197.

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Reflection based analysis has been used in previous research for various objectives. Materials classification is one of them. Basically, each material consists of two types of reflections: surface and sub-surface. To separate these two reflections, polarized light could be applied. Previously, multi-reflections characteristics were analyzed using polarized light to classify objects such as between metals and non-metals. However, no trial has been done using the same method to distinguish real and fake faces that could be used to combat spoofing attempts in face biometric system. Since human skin is multi layers structure, it also produces multi reflections. In this paper, driven by the theory, surface and sub-surface reflections of both genuine human face and paper face mask were statistically examined. In addition, iPad displayed face images were also used as spoofing attempts. Images of genuine and spoofing faces were captured using polarized light under two different polarization angles: 0 and 90 degrees. Each angle captured images with surface and sub-surface reflections, accordingly. Those reflections were analyzed based on the mean, standard deviation, skewness and kurtosis. Modality distribution of each image was also studied using another method called the bimodality coefficient (BC). From the results, it is not possible to distinguish between genuine face and printed photos because of the multi reflections’ similarities. However, iPad displayed face images have been successfully identified as spoofing trials.

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Ramon, Meike, Luca Vizioli, Joan Liu-Shuang, and Bruno Rossion. "Neural microgenesis of personally familiar face recognition." Proceedings of the National Academy of Sciences 112, no. 35 (August 17, 2015): E4835—E4844. http://dx.doi.org/10.1073/pnas.1414929112.

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Despite a wealth of information provided by neuroimaging research, the neural basis of familiar face recognition in humans remains largely unknown. Here, we isolated the discriminative neural responses to unfamiliar and familiar faces by slowly increasing visual information (i.e., high-spatial frequencies) to progressively reveal faces of unfamiliar or personally familiar individuals. Activation in ventral occipitotemporal face-preferential regions increased with visual information, independently of long-term face familiarity. In contrast, medial temporal lobe structures (perirhinal cortex, amygdala, hippocampus) and anterior inferior temporal cortex responded abruptly when sufficient information for familiar face recognition was accumulated. These observations suggest that following detailed analysis of individual faces in core posterior areas of the face-processing network, familiar face recognition emerges categorically in medial temporal and anterior regions of the extended cortical face network.

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47

Lee, Chang-Young, Soo-Kyung Lee, and In-Tae Ko. "The Analysis of Face-off in Korean Men's National Ice Hockey Team Games." Korean Journal of Sports Science 28, no. 2 (April 30, 2019): 1215–24. http://dx.doi.org/10.35159/kjss.2019.04.28.2.1215.

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48

Briozzo, Adriana C., and Domingo A. Tarzia. "Exact Solutions for Nonclassical Stefan Problems." International Journal of Differential Equations 2010 (2010): 1–19. http://dx.doi.org/10.1155/2010/868059.

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We consider one-phase nonclassical unidimensional Stefan problems for a source functionFwhich depends on the heat flux, or the temperature on the fixed facex=0. In the first case, we assume a temperature boundary condition, and in the second case we assume a heat flux boundary condition or a convective boundary condition at the fixed face. Exact solutions of a similarity type are obtained in all cases.

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Mende-Siedlecki, Peter, Sara C. Verosky, Nicholas B. Turk-Browne, and Alexander Todorov. "Robust Selectivity for Faces in the Human Amygdala in the Absence of Expressions." Journal of Cognitive Neuroscience 25, no. 12 (December 2013): 2086–106. http://dx.doi.org/10.1162/jocn_a_00469.

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There is a well-established posterior network of cortical regions that plays a central role in face processing and that has been investigated extensively. In contrast, although responsive to faces, the amygdala is not considered a core face-selective region, and its face selectivity has never been a topic of systematic research in human neuroimaging studies. Here, we conducted a large-scale group analysis of fMRI data from 215 participants. We replicated the posterior network observed in prior studies but found equally robust and reliable responses to faces in the amygdala. These responses were detectable in most individual participants, but they were also highly sensitive to the initial statistical threshold and habituated more rapidly than the responses in posterior face-selective regions. A multivariate analysis showed that the pattern of responses to faces across voxels in the amygdala had high reliability over time. Finally, functional connectivity analyses showed stronger coupling between the amygdala and posterior face-selective regions during the perception of faces than during the perception of control visual categories. These findings suggest that the amygdala should be considered a core face-selective region.

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CHOI, JAE-YOUNG, TAEG-KEUN WHANGBO, YOUNG-GYU YANG, MURLIKRISHNA VISWANATHAN, and NAK-BIN KIM. "POSE-EXPRESSION NORMALIZATION FOR FACE RECOGNITION USING CONNECTED COMPONENTS ANALYSIS." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 06 (September 2006): 869–81. http://dx.doi.org/10.1142/s0218001406005010.

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Accurate measurement of poses and expressions can increase the efficiency of recognition systems by avoiding the recognition of spurious faces. This paper presents a novel and robust pose-expression invariant face recognition method in order to improve the existing face recognition techniques. First, we apply the TSL color model for detecting facial region and estimate the vector X-Y-Z of face using connected components analysis. Second, the input face is mapped by a deformable 3D facial model. Third, the mapped face is transformed to the frontal face which appropriates for face recognition by the estimated pose vector and action unit of expression. Finally, the damaged regions which occur during the process of normalization are reconstructed using PCA. Several empirical tests are used to validate the application of face detection model and the method for estimating facial poses and expression. In addition, the tests suggest that recognition rate is greatly boosted through the normalization of the poses and expression.

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