Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
An improved face recognition technique based on modular PCA approach
Pattern Recognition Letters
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Weighted Sub-Gabor for face recognition
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Letters: Local ridge regression for face recognition
Neurocomputing
An adaptively weighted sub-pattern locality preserving projection for face recognition
Journal of Network and Computer Applications
Gabor feature based face recognition using supervised locality preserving projection
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
A Comparative Study of Local Matching Approach for Face Recognition
IEEE Transactions on Image Processing
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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In this paper, a novel local matching algorithm based on supervised locality preserving projections (LM-SLPP) is proposed for human face recognition. Unlike the holistic face recognition methods which operates directly on the whole face images and obtains a global face features, the proposed LM-SLPP operates on sub-patterns partitioned from the original whole face image and separately extracts corresponding local sub-features from them. In our method, the input face images are firstly divided into several sub-images. Then, the supervised locality preserving projections is applied on each sub-image set for feature extraction. At last, the nearest neighbor classifier combined with major voting is utilized to classify the new face images. The efficiency of the proposed algorithm is demonstrated by experiments on Yale and YaleB face databases. Experimental results show that LM-SLPP outperforms other holistic and sub-pattern based methods.