Support vector machines applied to face recognition
Proceedings of the 1998 conference on Advances in neural information processing systems II
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian tangent shape model: Estimating shape and pose parameters via bayesian inference
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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This poster presents experimental results of three face recognition methods -- Support Vector Machine (SVM), Local Binary Pattern (LBP)-based, and Sparse Represented-based Classification (SRC). We will show the experimental results based on AR face database and on home photos. The experiments show that the three algorithms can achieve over 85% recognition rate in AR database. However, the recognition rate is extremely reduced in home photos. SVM and SRC-based method encounter challenges of selecting training model while LBP-based method encounters the challenge of merging over scattered clusters. Our goal is to improve the accuracy and efficiency especially in home photos based on the three methods.