Extension of higher order local autocorrelation features
Pattern Recognition
An experimental evaluation of linear and kernel-based classifiers for face recognition
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Specific sensors for face recognition
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Image space I3 and eigen curvature for illumination insensitive face detection
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Recent advances in subspace analysis for face recognition
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Robust face recognition using the GAP feature
Pattern Recognition
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We report the results of a comparative study on subspace analysis methods for face recognition. In particular, we have studied four different subspace representations and their 'kernelized' versions if available. They include both unsupervised methods such as principal component analysis (PCA) and independent component analysis (ICA), and supervised methods such as Fisher discriminant analysis (FDA) and probabilistic PCA (PPCA) used in a discriminative manner. The 'kernelized' versions of these methods provide subspaces of high-dimensional feature spaces induced by non-linear mappings. To test the effectiveness of these subspace representations, we experiment on two databases with three typical variations of face images, i.e., pose, illumination and facial expression changes. The comparison of these methods applied to different variations in face images offers a comprehensive view of all the subspace methods currently used in face recognition.