A wearable face recognition system for individuals with visual impairments
Proceedings of the 7th international ACM SIGACCESS conference on Computers and accessibility
Learning the best subset of local features for face recognition
Pattern Recognition
Improved face representation by nonuniform multilevel selection of Gabor convolution features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multi-view face segmentation using fusion of statistical shape and appearance models
Computer Vision and Image Understanding
Application of Kekre's fast code book generation algorithm for face recognition
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Face recognition via direct search optimized Gabor filters
ICOSSE'06 Proceedings of the 5th WSEAS international conference on System science and simulation in engineering
Optimal batch selection for active learning in multi-label classification
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Face description for perceptual user interfaces
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
ASB'03 Proceedings of the 1st international conference on Advanced Studies in Biometrics
Robust 3D face recognition from expression categorisation
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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One of the major difficulties in face recognition systems is the in-depth pose variation problem. Most face recognition approaches assume that the pose of the face is known. In this work, we have designed a feature based pose estimation and face recognition system using 2D Gabor wavelets as local feature information. The difference of our system from the existing ones lies in its simplicity and its intelligent sampling of local features. Intelligent feature selection can be carried out by learning a set of parameters where the aim is the optimal performance of the overall system. In this paper, we give comparative analysis of the performance of our system with the standard modular Eigenfaces approach and show that local feature based approach improved the performance of both pose estimation and face recognition. For efficient coding, we have employed Principal Component Analysis (PCA) to the outputs of local feature vectors. Intelligent feature selection also reduced the space and time complexity of the system while retaining almost the same estimation and recognition accuracy.