Fundamentals of digital image processing
Fundamentals of digital image processing
Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust recognition using eigenimages
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dealing with occlusions in the eigenspace approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Journal of Cognitive Neuroscience
Recognizing faces under facial expression variations and partial occlusions
SIP'08 Proceedings of the 7th WSEAS International Conference on Signal Processing
Illumination Invariant Face Recognition under Various Facial Expressions and Occlusions
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Sensitivity analysis of partitioning-based face recognition algorithms on occlusions
AEE'07 Proceedings of the 6th conference on Applications of electrical engineering
A multi-level supporting scheme for face recognition under partial occlusions and disguise
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Learning kernel subspace classifier
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Reconstruction of occluded facial images using asymmetrical Principal Component Analysis
Integrated Computer-Aided Engineering
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In this paper, we propose a novel occlusion invariant face recognition algorithm based on Selective Local Nonnegative Matrix Factorization (S-LNMF) technique. The proposed algorithm is composed of two phases; the occlusion detection phase and the selective LNMF-based recognition phase. We use local approach to effectively detect partial occlusion in the input face image. A face image is first divided into a finite number of disjointed local patches, and then each patch is represented by PCA (Principal Component Analysis), obtained by corresponding occlusion-free patches of training images. And 1-NN threshold classifier was used for occlusion detection for each patch in the corresponding PCA space. In the recognition phase, by employing the LNMF-based face representation, we exclusively use the LNMF bases of occlusion-free image patches for face recognition. Euclidean nearest neighbor rule is applied for the matching. Experimental results demonstrate that the proposed local patch-based occlusion detection technique and S-LNMF-based recognition algorithm works well and the performance is superior to other conventional approaches.