Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
On the Euclidean Distance of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Theoretical Investigation on Post-Processed LDA for Face and Palmprint Recognition
CIS '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security
Regularization of LDA for face recognition: a post-processing approach
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Bidirectional PCA with assembled matrix distance metric for image recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Subspace face recognition methods have been widely investigated in the last few decades. Since the pixels of an image are spatially correlated and facial images are generally considered to be spatially smoothing, several spatially smooth subspace methods have been proposed for face recognition. In this paper, we first survey the progress and problems in current spatially smooth subspace face recognition methods. Using the penalized subspace learning framework, we then proposed two novel penalty functions, Laplacian of Gaussian (LOG) and Derivative of Gaussian (DOG), for subspace face recognition. LOG and DOG penalties introduce a scale parameter, and thus are more flexible in controlling the degree of smoothness. Experimental results indicate that the proposed methods are effective for face recognition, and achieve higher recognition accuracy than the original subspace methods.