Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond Eigenfaces: Probabilistic Matching for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Principal Manifolds and Bayesian Subspaces for Visual Recognition
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Recognition of Asymmetric Facial Action Unit Activities and Intensities
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
An embedded HMM-based approach for face detection and recognition
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
IEEE Transactions on Pattern Analysis and Machine Intelligence
2D and 3D face recognition: A survey
Pattern Recognition Letters
Component-based LDA face description for image retrieval and MPEG-7 standardisation
Image and Vision Computing
Boosted independent features for face expression recognition
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Recent advances in subspace analysis for face recognition
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
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In this paper, we propose an ICA(Indepdendent Component Analysis) based face recognition algorithm, which is robust to illumination and pose variation. Generally, it is well known that the first few eigenfaces represent illumination variation rather than identity. Most PCA(Principal Component Analysis)-based methods have overcome illumination variation by discarding the projection to a few leading eigenfaces. The space spanned after removing a few leading eigenfaces is called the "residual face space". We found that ICA in the residual face space provides more efficient encoding in terms of redundancy reduction and robustness to pose variation as well as illumination variation, owing to its ability to represent non-Gaussian statistics. Moreover, a face image is separated into several facial components, local spaces, and each local space is represented by the ICA bases (independent components) of its corresponding residual space. The statistical models of face images in local spaces are relatively simple and facilitate classification by a linear encoding. Various experimental results show that the accuracy of face recognition is significantly improved by the proposed method under large illumination and pose variations.