Proceedings ArticleDOI
On video based face recognition through adaptive sparse dictionary
Naimul Mefraz Khan,Xiaoming Nan,Azhar Quddus,Edward Rosales,Ling Guan +4 more
- Vol. 1, pp 1-6
TLDR
This paper proposes a video-based face recognition method which improves upon the sparse representation framework with an intelligent and adaptive sparse dictionary that updates the current probe image into the training matrix based on continuously monitoring the probe video through a novel confidence criterion and a Bayesian inference scheme.Abstract:
Sparse representation-based face recognition has gained considerable attention recently due to its robustness against illumination and occlusion. Recognizing faces from videos has become a topic of importance to alleviate the limit of information content in still images. However, the sparse recognition framework is not applicable to video-based face recognition due to its sensitivity towards pose and alignment changes. In this paper, we propose a video-based face recognition method which improves upon the sparse representation framework. Our key contribution is an intelligent and adaptive sparse dictionary that updates the current probe image into the training matrix based on continuously monitoring the probe video through a novel confidence criterion and a Bayesian inference scheme. Due to this novel approach, our method is robust to pose and alignment and hence can be used to recognize faces from unconstrained videos successfully. Moreover, in a moving scene, camera angle, illumination and other imaging conditions may change quickly leading to performance loss in accuracy. In such situations, it is impractical to re-enroll the individual and re-train the classifiers on a continuous basis. Our novel approach addresses these practical issues. Experimental results on the well known YouTube Face database demonstrates the effectiveness of our method.read more
Citations
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Journal ArticleDOI
Face Verification via Learned Representation on Feature-Rich Video Frames
TL;DR: Experimental analysis suggests that the proposed feature-richness-based frame selection offers noticeable and consistent performance improvement compared with frontal only frames, random frames, or frame selection using perceptual no-reference image quality measures and joint feature learning in SDAE and sparse and low rank regularization in DBM helps in improving face verification performance.
Journal ArticleDOI
Face recognition-based real-time system for surveillance
Fahad Parvez Mahdi,Md. Mahmudul Habib,Md. Atiqur Rahman Ahad,Susan McKeever,A.S.M. Moslehuddin,Pandian Vasant +5 more
Dissertation
Unraveling representations for face recognition : from handcrafted to deep learning
TL;DR: This dissertation proposes novel feature extraction and fusion paradigms along with improvements to existing methodologies in order to address the challenge of unconstrained face recognition and presents a novel methodology to improve the robustness of such algorithms in a generalizable manner.
References
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Proceedings ArticleDOI
Manifold-Manifold Distance with application to face recognition based on image set
TL;DR: The proposed MMD method outperforms the competing methods on the task of Face Recognition based on Image Set, and a novel manifold learning approach is proposed, which expresses a manifold by a collection of local linear models, each depicted by a subspace.
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Face Recognition from Long-Term Observations
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Fast $\ell_{1}$ -Minimization Algorithms for Robust Face Recognition
TL;DR: In this paper, the authors focus on the numerical implementation of a sparsity-based classification framework in robust face recognition, where sparse representation is sought to recover human identities from high-dimensional facial images that may be corrupted by illumination, facial disguise, and pose variation.
Proceedings ArticleDOI
Video-based face recognition using adaptive hidden Markov models
Xiaoming Liu,Tsuhan Cheng +1 more
TL;DR: This paper proposes to use adaptive hidden Markov models (HMM) to perform video-based face recognition and shows that the proposed algorithm results in better performance than using majority voting of image-based recognition results.
Proceedings ArticleDOI
Probabilistic Elastic Matching for Pose Variant Face Verification
TL;DR: This work proposes a joint Bayesian adaptation algorithm to adapt the universally trained GMM to better model the pose variations between the target pair of faces/face tracks, which consistently improves face verification accuracy.