Relevant bibliographies by topics / Unconstrained face recognition

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Unconstrained face recognition'

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Published: 4 May 2024

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Journal articles on the topic "Unconstrained face recognition"

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Deng, Weihong, Jiani Hu, Zhongjun Wu, and Jun Guo. "Lighting-aware face frontalization for unconstrained face recognition." Pattern Recognition 68 (August 2017): 260–71. http://dx.doi.org/10.1016/j.patcog.2017.03.024.

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Masi, Iacopo, Anh Tuấn Trần, Tal Hassner, Gozde Sahin, and Gérard Medioni. "Face-Specific Data Augmentation for Unconstrained Face Recognition." International Journal of Computer Vision 127, no. 6-7 (April 1, 2019): 642–67. http://dx.doi.org/10.1007/s11263-019-01178-0.

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Tyagi, Ranbeer, Geetam Singh Tomar, and Laxmi Shrivastava. "Unconstrained Face Recognition Quality: A Review." International Journal of Signal Processing, Image Processing and Pattern Recognition 9, no. 11 (November 30, 2016): 199–210. http://dx.doi.org/10.14257/ijsip.2016.9.11.18.

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Vinay, A., Abhijay Gupta, Aprameya Bharadwaj, Arvind Srinivasan, K. N. Balasubramanya Murthy, and S. Natarajan. "Unconstrained Face Recognition using Bayesian Classification." Procedia Computer Science 143 (2018): 519–27. http://dx.doi.org/10.1016/j.procs.2018.10.425.

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Rifaee, Mustafa, Mohammad Al Rawajbeh, Basem AlOkosh, and Farhan AbdelFattah. "A New approach to Recognize Human Face Under Unconstrained Environment." International Journal of Advances in Soft Computing and its Applications 14, no. 2 (July 20, 2022): 2–13. http://dx.doi.org/10.15849/ijasca.220720.01.

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Human face is considered as one of the most useful traits in biometrics, and it has been widely used in education, security, military and many other applications. However, in most of currently deployed face recognition systems ideal imaging conditions are assumed; to capture a fully featured images with enough quality to perform the recognition process. As the unmasked face will have a considerable impact on the numbers of new infections in the era of COVID-19 pandemic, a new unconstrained partial facial recognition method must be developed. In this research we proposed a mask detection method based on HOG (Histogram of Gradient) features descriptor and SVM (Support Vector Machine) to determine whether the face is masked or not, the proposed method was tested over 10000 randomly selected images from Masked Face-Net database and was able to correctly classify 98.73% of the tested images. Moreover, and to extract enough features from partially occluded face images, a new geometrical features extraction algorithm based on Contourlet transform was proposed. The method achieved 97.86% recognition accuracy when tested over 4784 correctly masked face images from Masked Face-Net database. Keywords: Facial Recognition, Unconstraint conditions, masked faces, HOG, Support Vector Machine.
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Yu, Aihua, Gang Li, Beiping Hou, Hongan Wang, and Gaoya Zhou. "A novel framework for face recognition using robust local representation–based classification." International Journal of Distributed Sensor Networks 15, no. 3 (March 2019): 155014771983608. http://dx.doi.org/10.1177/1550147719836082.

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Face recognition via representation-based classification is a trending technique in the recent years. However, the recognition performance of the systems using such a technique degrades in an unconstrained environment. In this article, a novel framework is proposed for representation-based face recognition. To deal with the unconstrained environment, a pre-process is used to frontalize face images, and aligned downsampling local binary pattern features of the frontalized images are used for classification. A dimension reduction is then adopted in order to reduce the computation complexity via an optimized projection matrix. The recognition is carried out using an improved robust sparse coding algorithm. Such an algorithm is expected to avoid the overfitting problem. The open-universe test on labeled faces in the wild data sets shows that the recognition rate of the proposed system can reach 95% with a recall rate of 80%, which is best among those representation-based classification face recognition systems.
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TORBATI, Ali, and Önsen TOYGAR. "MASKED AND UNMASKED FACE RECOGNITION ON UNCONSTRAINED FACIAL IMAGES USING HAND-CRAFTED METHODS." Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 26, Özel Sayı (December 12, 2023): 1133–39. http://dx.doi.org/10.17780/ksujes.1339868.

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In this study, the face recognition task is applied on masked and unmasked faces using hand-crafted methods. Due to COVID-19 and masks, facial identification from unconstrained images became a hot topic. To avoid COVID-19, most people use masks outside. In many cases, typical facial recognition technology is useless. The majority of contemporary advanced face recognition methods are based on deep learning, which primarily relies on a huge number of training examples, however, masked face recognition may be investigated using hand-crafted approaches at a lower computing cost than using deep learning systems. A low-cost system is intended to be constructed for recognizing masked faces and compares its performance to that of face recognition systems that do not use masks. The proposed method fuses hand-crafted methods using feature-level fusion strategy. This study compares the performance of masked and unmasked face recognition systems. The experiments are undertaken on two publicly accessible datasets for masked face recognition: Masked Labeled Faces in the Wild (MLFW) and Cross-Age Labeled Faces in the Wild (CALFW). The best accuracy is achieved as 94.8% on MLFW dataset. The rest of the results on different train and test sets from CALFW and MLFW datasets are encouraging compared to the state-of-the-art models.
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Ruan, Shuai, Chaowei Tang, Xu Zhou, Zhuoyi Jin, Shiyu Chen, Haotian Wen, Hongbin Liu, and Dong Tang. "Multi-Pose Face Recognition Based on Deep Learning in Unconstrained Scene." Applied Sciences 10, no. 13 (July 7, 2020): 4669. http://dx.doi.org/10.3390/app10134669.

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At present, deep learning drives the rapid development of face recognition. However, in the unconstrained scenario, the change of facial posture has a great impact on face recognition. Moreover, the current model still has some shortcomings in accuracy and robustness. The existing research has formulated two methods to solve the above problems. One method is to model and train each pose separately. Then, a fusion decision will be made. The other method is to make “frontal” faces on the image or feature level and transform them into “frontal” face recognition. Based on the second idea, we propose a profile to the frontal revise mapping (PTFRM) module. This module realizes the revision of arbitrary poses on the feature level and transforms the multi-pose features into an approximate frontal representation to enhance the recognition ability of the existing recognition models. Finally, we evaluate the PTFRM on unconstrained face validation benchmark datasets such as Labeled Faces in the Wild (LFW), Celebrities in Frontal Profile (CFP), and IARPA Janus Benchmark A(IJB-A). Results show that the chosen method for this study achieves good performance.
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Tong, Ying, Jiachao Zhang, and Rui Chen. "Discriminative Sparsity Graph Embedding for Unconstrained Face Recognition." Electronics 8, no. 5 (May 7, 2019): 503. http://dx.doi.org/10.3390/electronics8050503.

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In this paper, we propose a new dimensionality reduction method named Discriminative Sparsity Graph Embedding (DSGE) which considers the local structure information and the global distribution information simultaneously. Firstly, we adopt the intra-class compactness constraint to automatically construct the intrinsic adjacent graph, which enhances the reconstruction relationship between the given sample and the non-neighbor samples with the same class. Meanwhile, the inter-class compactness constraint is exploited to construct the penalty adjacent graph, which reduces the reconstruction influence between the given sample and the pseudo-neighbor samples with the different classes. Then, the global distribution constraints are introduced to the projection objective function for seeking the optimal subspace which compacts intra-classes samples and alienates inter-classes samples at the same time. Extensive experiments are carried out on AR, Extended Yale B, LFW and PubFig databases which are four representative face datasets, and the corresponding experimental results illustrate the effectiveness of our proposed method.
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Agrawal, Amrit Kumar, and Yogendra Narain Singh. "Unconstrained face recognition using deep convolution neural network." International Journal of Information and Computer Security 12, no. 2/3 (2020): 332. http://dx.doi.org/10.1504/ijics.2020.10026788.

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Dissertations / Theses on the topic "Unconstrained face recognition"

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Zhou, Shaohua. "Unconstrained face recognition." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/1800.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2004.
Thesis research directed by: Electrical Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Wei, Xingjie. "Unconstrained face recognition with occlusions." Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/66778/.

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Face recognition is one of the most active research topics in the interdisciplinary areas of biometrics, pattern recognition, computer vision and machine learning. Nowadays, there has been significant progress on automatic face recognition in controlled conditions. However, the performance in unconstrained conditions is still unsatisfactory. Face recognition systems in real-world environments often have to confront uncontrollable and unpredictable conditions such as large changes in illumination, pose, expression and occlusions, which introduce more intra-class variations and degrade the recognition performance. Compared with these factor related problems, the occlusion problem is relatively less studied in the research community. The overall goal of this thesis is to design robust algorithms for face recognition with occlusions in unconstrained environments. In uncontrollable environments, the occlusion preprocessing and detection are generally very difficult. Compared with previous works, we focus on directly performing recognition with the presence of occlusions. We deal with the occlusion problem in two directions and propose three novel algorithms to handle the occlusions in face images while also considering other factors. We propose a reconstruction based method structured sparse representation based face recognition when multiple gallery images are available for each subject. We point out that the non-zeros entries in the occlusion coefficient vector also have a cluster structure and propose a structured occlusion dictionary for better modelling them. On the other hand, we propose a local matching based method Dynamic Image-to-Class Warping (DICW) when the number of gallery images per subject is limited. DICW considers the inherent structure of the face and the experimental results confirm that the facial order is critical for recognition. In addition, we further propose a novel method fixations and saccades based classification when only one single gallery image is available for each subject. It is an extension of DICW and can be also applied to deal with other problems in face recognition caused by local deformations. The proposed algorithms are evaluated on standard face databases with various types occlusions and experimental results confirmed their effectiveness. We also consider several important and practical problems which are less noticed (i.e., coupled factors, occlusions in gallery or/and probe sets and the single sample per person problem) in face recognition and provide solutions to them.
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Juefei-Xu, Felix. "Unconstrained Periocular Face Recognition: From Reconstructive Dictionary Learning to Generative Deep Learning and Beyond." Research Showcase @ CMU, 2018. http://repository.cmu.edu/dissertations/1189.

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Many real-world face recognition tasks are under unconstrained conditions such as off-angle pose variations, illumination variations, facial occlusion, facial expression, etc. In this work, we are focusing on the real-world scenarios where only the periocular region of a face is visible such as in the ISIS case. In Part I of the dissertation, we will showcase the face recognition capability based on the periocular region, which we call the periocular face recognition. We will demonstrate that face matching using the periocular region directly is more robust than the full face in terms of age-tolerant face recognition, expression-tolerant face recognition, pose-tolerant face recognition, as well as contains more cues for determining the gender information of a subject. In this dissertation, we will study direct periocular matching more comprehensively and systematically using both shallow and deep learning methods. Based on this, in Part II and Part III of the dissertation, we will continue to explore an indirect way of carrying out the periocular face recognition: periocular-based full face hallucination, because we want to capitalize on the powerful commercial face matchers and deep learning-based face recognition engines which are all trained on large-scale full face images. The reproducibility and feasibility of re-training for a proprietary facial region, such as the periocular region, is relatively low, due to the nonopen source nature of commercial face matchers as well as the amount of training data and computation power required by the deep learning based models. We will carry out the periocular-based full face hallucination based on two proposed reconstructive dictionary learning methods, including the dimensionally weighted K-SVD (DW-KSVD) dictionary learning approach and its kernel feature space counterpart using Fastfood kernel expansion approximation to reconstruct high-fidelity full face images from the periocular region, as well as two proposed generative deep learning approaches that build upon deep convolutional generative adversarial networks (DCGAN) to generate the full face from the periocular region observations, including the Gang of GANs (GoGAN) method and the discriminant nonlinear many-to-one generative adversarial networks (DNMM-GAN) for applications such as the generative open-set landmark-free frontalization (Golf) for faces and universal face optimization (UFO), which tackles an even broader set of problems than periocular based full face hallucination. Throughout Parts I-III, we will study how to handle challenging realworld scenarios such as unconstrained pose variations, unconstrained illumination conditions, and unconstrained low resolution of the periocular and facial images. Together, we aim to achieve unconstrained periocular face recognition through both direct periocular face matching and indirect periocular-based full face hallucination. In the final Part IV of the dissertation, we will go beyond and explore several new methods in deep learning that are statistically efficient for generalpurpose image recognition. Methods include the local binary convolutional neural networks (LBCNN), the perturbative neural networks (PNN), and the polynomial convolutional neural networks (PolyCNN).
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Lopes, Daniel Pedro Ferreira. "Face verication for an access control system in unconstrained environment." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/23395.

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Mestrado em Engenharia Eletrónica e Telecomunicações
O reconhecimento facial tem vindo a receber bastante atenção ao longo dos últimos anos não só na comunidade cientifica, como também no ramo comercial. Uma das suas várias aplicações e o seu uso num controlo de acessos onde um indivíduo tem uma ou várias fotos associadas a um documento de identificação (também conhecido como verificação de identidade). Embora atualmente o estado da arte apresente muitos estudos em que tanto apresentam novos algoritmos de reconhecimento como melhorias aos já desenvolvidos, existem mesmo assim muitos problemas ligados a ambientes não controlados, a aquisição de imagem e a escolha dos algoritmos de deteção e de reconhecimento mais eficazes. Esta tese aborda um ambiente desafiador para a verificação facial: um cenário não controlado para o acesso a infraestruturas desportivas. Uma vez que não existem condições de iluminação controladas nem plano de fundo controlado, isto torna um cenário complicado para a implementação de um sistema de verificação facial. Esta tese apresenta um estudo sobre os mais importantes algoritmos de detecção e reconhecimento facial assim como técnicas de pré-processamento tais como o alinhamento facial, a igualização de histograma, com o objetivo de melhorar a performance dos mesmos. Também em são apresentados dois métodos para a aquisição de imagens envolvendo a seleção de imagens e calibração da câmara. São apresentados resultados experimentais detalhados baseados em duas bases de dados criadas especificamente para este estudo. No uso de técnicas de pré-processamento apresentadas, foi possível presenciar melhorias até 20% do desempenho dos algoritmos de reconhecimento referentes a verificação de identidade. Com os métodos apresentados para os testes ao ar livre, foram conseguidas melhorias na ordem dos 30%.
Face Recognition has been received great attention over the last years, not only on the research community, but also on the commercial side. One of the many uses of face recognition is its use on access control systems where a person has one or several photos associated to an Identi cation Document (also known as identity veri cation). Although there are many studies nowadays, both presenting new algorithms or just improvements of the already developed ones, there are still many open problems regarding face recognition in uncontrolled environments, from the image acquisition conditions to the choice of the most e ective detection and recognition algorithms, just to name a few. This thesis addresses a challenging environment for face veri cation: an unconstrained environment for sports infrastructures access. As there are no controlled lightning conditions nor controlled background, this makes a di cult scenario to implement a face veri cation system. This thesis presents a study of some of the most important facial detection and recognition algorithms as well as some pre-processing techniques, such as face alignment and histogram equalization, with the aim to improve their performance. It also introduces some methods for a more e cient image acquisition based on image selection and camera calibration, specially designed for addressing this problem. Detailed experimental results are presented based on two new databases created speci cally for this study. Using pre-processing techniques, it was possible to improve the recognition algorithms performances up to 20% regarding veri cation results. With the methods presented for the outdoor tests, performances had improvements up to 30%
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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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Vu, Ngoc-Son. "Contributions à la reconnaissance de visages à partir d'une seule image et dans un contexte non-contrôlé." Phd thesis, Grenoble INPG, 2010. https://theses.hal.science/tel-00574547v1.

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Bien qu'ayant suscité des recherches depuis 30 ans, le problème de la reconnaissance de visages en contexte de vidéosurveillance, sachant qu'une seule image par individu est disponible pour l'enrôlement, n'est pas encore résolu. Dans ce contexte, les deux défis les plus difficiles à relever consistent à développer des algorithmes robustes aux variations d'illumination et aux variations de pose. De plus, il y a aussi une contrainte forte sur la complexité en temps et en occupation mémoire des algorithmes à mettre en oeuvre dans de tels systèmes. Le travail développé dans cette thèse apporte plusieurs avancées innovantes dans ce contexte de reconnaissance faciale en vidéosurveillance. Premièrement, une méthode de normalisation des variations d'illumination visant à simuler les performances de la rétine est proposée en tant que pré-traitement des images faciales. Deuxièmement, nous proposons un nouveau descripteur appelé POEM (Patterns of Oriented Edge Magnitudes) destiné à représenter les structures locales d'une image. Ce descripteur est discriminant, robuste aux variations extérieures (variations de pose, d'illumination, d'expression, d'âge que l'on rencontre souvent avec les visages). Troisièmement, un modèle statistique de reconnaissance de visages en conditions de pose variables, centré sur une modélisation de la manière dont l'apparence du visage évolue lorsque le point de vue varie, est proposé. Enfin, une nouvelle approche visant à modéliser les relations spatiales entre les composantes du visage est présentée. A l'exception de la dernière approche, tous les algorithmes proposés sont très rapides à calculer et sont donc adaptés à la contrainte de traitement temps réel des systèmes de vidéosurveillance
Although having been an active research topic for 30 years, recognizing a person from surveillance having seen only one image is unsolved. Within this context, the two greatest challenges are the variations of pose and illumination. Moreover, there are strict constraints upon the complexity in both terms of computational time and stockage requirements. The work developed throughout this dissertation gives several advantages in the context of real-time and unconstrained face recognition. Firstly, an illumination normalization method simulating the performance of human retina is proposed as preprocessing algorithm. Secondly, we propose novel features called POEM (Patterns of Oriented Edge Magnitudes) for representing a local image structure. This descriptor is discriminative and robust to exterior variations (variations of pose, illumination, expression and pose that we always see when dealing with face images). Thirdly, a statistical model for robust face recognition across poses, entered on modeling how facial patch appearance changes as the viewpoint varies, is proposed. Finally, a novel approach modeling the spatial relationships between face components is developed. Except the last algorithm, all proposed methods are very fast and are therefore suitable for the constraints upon real-time of surveillance applications
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Mudunuri, Sivaram Prasad. "Face Recognition in Unconstrained Environment." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/5113.

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The goal of computer vision is to provide the ability to machines to understand image data and infer the useful information from it. The inferences highly depend on the quality of the image data. But in many real-world applications, we encounter poor quality images which have low discriminative power which affects the performance of computer vision algorithms. In particular, in the field of Biometrics, the performance of face recognition systems are significantly affected when the face images have poor resolution and are captured under uncontrolled pose and illumination conditions as in surveillance settings. In this thesis, we propose algorithms to match the low-resolution probe images captured under non frontal pose and poor illumination conditions with the high-resolution gallery faces captured in frontal pose and good illuminations which are often available during enrollment. Many of the standard metric learning and dictionary learning approaches perform quite well in matching faces across different domains but they require the locations of several landmark points like corners of eyes, nose and mouth etc. both during training and testing. This is a difficult task especially for low-resolution images under non-frontal pose. In the first algorithm of this thesis, we propose a multi-dimensional scaling based approach to learn a common transformation matrix for the entire face which simultaneously transforms the facial features of the low-resolution and the high-resolution training images such that the distance between them approximates the distance had both the images been captured under the same controlled imaging conditions. It is only during the training stage that we need locations of different fiducial points to learn the transformation matrix. To overcome the computational complexity of the algorithm, we further proposed a reference-based face recognition approach with a trade-off on recognition performance. In our second approach in this thesis, we propose a novel deep convolutional neural network architecture to address the low-resolution face recognition by systematically introducing different kinds of constraints at different layers of the architecture so that the approach can recognize low-resolution images as well as generalize well to images of unseen categories. Though coupled dictionary learning has emerged as a powerful technique for matching data samples of cross domains, most of the frameworks demand one-to-one paired training samples. In practical surveillance face recognition problems, there can be just one high-resolution image and many low resolution images of each subject for training in which there is no exact one-to-one correspondence in the images from two domains. The third algorithm proposes an orthogonal dictionary learning and alignment approach for handling this problem. In this part, we also address the heterogeneous face recognition problem where the gallery images are captured from RGB camera and the probe images are captured from near-infrared (NIR) camera. We further explored the more challenging problem of low-resolution heterogeneous face recognition where the probe faces are low-resolution NIR images since recently, NIR images are increasingly being captured for recognizing faces in low-light/night-time conditions. We developed a re-ranking framework to address the problem. To further encourage the research in this field, we have also collected our own database HPR (Heterogeneous face recognition across Pose and Resolution) which has facial images captured from two surveillance quality NIR cameras and one high-resolution visible camera, with significant variations in head pose and resolution. Extensive related experiments are conducted on each of the proposed approaches to demonstrate their effectiveness and usefulness
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Reigoto, Anabela Machado. "Face Detection and Recognition in Unconstrained Scenarios." Master's thesis, 2019. https://hdl.handle.net/10216/122842.

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Sanyal, Soubhik. "Discriminative Descriptors for Unconstrained Face and Object Recognition." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4177.

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Face and object recognition is a challenging problem in the field of computer vision. It deals with identifying faces or objects form an image or video. Due to its numerous applications in biometrics, security, multimedia processing, on-line shopping, psychology and neuroscience, automated vehicle parking systems, autonomous driving and machine inspection, it has drawn attention from a lot of researches. Researchers have studied different aspects of this problem. Among them pose robust matching is a very important problem with various applications like recognizing faces and objects in uncontrolled scenarios in which the images appear in wide variety of pose and illumination conditions along with low resolution. In this thesis, we propose three discriminative pose-free descriptors, Subspace Point Representation (DPF-SPR), Layered Canonical Correlated (DPF-LCC ) and Aligned Discriminative Pose Robust (ADPR) descriptor, for matching faces and objects across pose. They are also robust for recognition in low resolution and varying illumination. We use training examples at very few poses to generate virtual intermediate pose subspaces. An image is represented by a feature set obtained by projecting its low-level feature on these subspaces. This way we gather more information regarding the unseen poses by generating synthetic data and make our features more robust towards unseen pose variations. Then we apply a discriminative transform to make this feature set suitable for recognition for generating two of our descriptors namely DPF-SPR and DPF-LCC. In one approach, we transform it to a vector by using subspace to point representation technique which generates our DPF-SPR descriptors. In the second approach, layered structures of canonical correlated subspaces are formed, onto which the feature set is projected which generates our DPF-LCC descriptor. In a third approach we first align the remaining subspaces with the frontal one before learning the discriminative metric and concatenate the aligned discriminative projected features to generate ADPR. Experiments on recognizing faces and objects across varying pose are done. Specifically we have done experiments on MultiPIE and Surveillance Cameras Face database for face recognition and COIL-20 and RGB-D dataset for object recognition. We show that our approaches can even improve the recognition rate over the state-of-the-art deep learning approaches. We also perform extensive analysis of our three descriptors to get a better qualitative understanding. We compare with state-of-the-art to show the effectiveness of the proposed approaches.
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Freitas, Tiago Daniel Santos. "3D Face Recognition Under Unconstrained settings using Low-Cost Sensors." Master's thesis, 2016. https://repositorio-aberto.up.pt/handle/10216/84513.

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Books on the topic "Unconstrained face recognition"

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Unconstrained Face Recognition. Springer US, 2006. http://dx.doi.org/10.1007/978-0-387-29486-5.

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Chellappa, Rama, Shaohua Kevin Zhou, and Wenyi Zhao. Unconstrained Face Recognition. Springer, 2010.

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Unconstrained Face Recognition (International Series on Biometrics). Springer, 2005.

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Chellappa, Rama, Shaohua Kevin Zhou, and Wenyi Zhao. Unconstrained Face Recognition (International Series on Biometrics Book 5). Springer, 2006.

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Book chapters on the topic "Unconstrained face recognition"

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Ouanan, Hamid, Mohammed Ouanan, and Brahim Aksasse. "Myface: Unconstrained Face Recognition." In Lecture Notes in Networks and Systems, 86–94. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69137-4_9.

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Kocjan, Przemysław, and Khalid Saeed. "Face Recognition in Unconstrained Environment." In Biometrics and Kansei Engineering, 21–42. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5608-7_2.

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Gong, Xun, Jun Luo, and Zehua Fu. "Normalization for Unconstrained Pose-Invariant 3D Face Recognition." In Biometric Recognition, 1–8. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02961-0_1.

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Liu, Feng, Minchul Kim, Anil Jain, and Xiaoming Liu. "Controllable and Guided Face Synthesis for Unconstrained Face Recognition." In Lecture Notes in Computer Science, 701–19. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19775-8_41.

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Wu, Zhongjun, Weihong Deng, and Zhanfu An. "Illumination-Recovered Pose Normalization for Unconstrained Face Recognition." In Computer Vision – ACCV 2016, 217–33. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54187-7_15.

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Tome, Pedro, Ruben Vera-Rodriguez, Julian Fierrez, and Javier Ortega-García. "Variability Compensation Using NAP for Unconstrained Face Recognition." In Advances in Intelligent and Soft Computing, 129–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28765-7_17.

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Moghekar, Rajeshwar, and Sachin Ahuja. "Face Recognition in Unconstrained Environment Using Deep Learning." In Soft Computing for Intelligent Systems, 241–53. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1048-6_18.

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Martin, Michael, and Thirimachos Bourlai. "Unconstrained Face Recognition Using Cell Phone Devices: Faces in the Wild." In Advanced Sciences and Technologies for Security Applications, 129–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39489-9_7.

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Yan, Yan, Hanzi Wang, Cuihua Li, Chenhui Yang, and Bineng Zhong. "A Novel Unconstrained Correlation Filter and Its Application in Face Recognition." In Intelligent Science and Intelligent Data Engineering, 32–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36669-7_5.

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Ruiz-del-Solar, Javier, Rodrigo Verschae, Gabriel Hermosilla, and Mauricio Correa. "Thermal Face Recognition in Unconstrained Environments Using Histograms of LBP Features." In Local Binary Patterns: New Variants and Applications, 219–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39289-4_10.

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Conference papers on the topic "Unconstrained face recognition"

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Huang, Gary B., Manjunath Narayana, and Erik Learned-Miller. "Towards unconstrained face recognition." In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2008. http://dx.doi.org/10.1109/cvprw.2008.4562973.

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"Subtasks of Unconstrained Face Recognition." In International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004694201130121.

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Srisawasd, Wipawee, and Sartra Wongthanavasu. "Face Recognition In Unconstrained Environment." In 2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2018. http://dx.doi.org/10.1109/jcsse.2018.8457336.

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Saffar, Mohammad Taghi, Banafsheh Rekabdar, Sushil Louis, and Mircea Nicolescu. "Face recognition in unconstrained environments." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280803.

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Dong-Ju Kim, Sang-Heon Lee, Myoung-Kyu Sohn, Byungmin Kim, and Hyunduk Kim. "Face recognition in unconstrained environments." In 2013 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2013. http://dx.doi.org/10.1109/icce.2013.6486832.

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Shao, Xiaohu, Junliang Xing, Jiangjing Lv, Chunlin Xiao, Pengcheng Liu, Youji Feng, and Cheng Cheng. "Unconstrained Face Alignment Without Face Detection." In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2017. http://dx.doi.org/10.1109/cvprw.2017.258.

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Roth, Joseph, Yiying Tong, and Xiaoming Liu. "Unconstrained 3D face reconstruction." In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015. http://dx.doi.org/10.1109/cvpr.2015.7298876.

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Dao, Viet-Anh, Dang-Ha Nguyen, Viet-Bac Nguyen, Thom Tran Thi, and Hoang-Anh Nguyen The. "Face Recognition System for Unconstrained Condition." In 2023 International Conference on Advanced Technologies for Communications (ATC). IEEE, 2023. http://dx.doi.org/10.1109/atc58710.2023.10318921.

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Tyagi, Ranbeer, Geetam Singh Tomar, and Laxmi Shrivastava. "Unconstrained Face Recognition from Image Sequence." In 2023 IEEE World Conference on Applied Intelligence and Computing (AIC). IEEE, 2023. http://dx.doi.org/10.1109/aic57670.2023.10263884.

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Gunther, M., P. Hu, C. Herrmann, C. H. Chan, M. Jiang, S. Yang, A. R. Dhamija, et al. "Unconstrained Face Detection and Open-Set Face Recognition Challenge." In 2017 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2017. http://dx.doi.org/10.1109/btas.2017.8272759.

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