Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Independent Component Analysis and Support Vector Machines
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Kernel-based nonlinear blind source separation
Neural Computation
Face Recognition Using Kernel Based Fisher Discriminant Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Kernel independent component analysis
The Journal of Machine Learning Research
Recognizing faces with PCA and ICA
Computer Vision and Image Understanding - Special issue on Face recognition
Journal of Cognitive Neuroscience
Information maximization in face processing
Neurocomputing
Local Image Descriptors Using Supervised Kernel ICA
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Global and local preserving feature extraction for image categorization
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Image registration with regularized neural network
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
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Appearance-based approach is one of popular methods for face analysis. How to describe face appearance is a key issue for appearance based face analysis. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are two successful and well-studied linear unsupervised representation methods of face appearance. However, there exist complicate nonlinear variations in real face images due to pose, illumination, expression variations and so on, so it is inadequate for PCA and ICA to describe these nonlinear relations in real face images because of their linear properties in nature. In this paper, a nonlinear ICA is proposed to model face appearance, which combines the nonlinear kernel trick with ICA. First, the kernel trick is employed to project the input image data into a high-dimensional implicit feature space F with a nonlinear mapping, and then ICA is performed in F to produce nonlinear independent components of input data. We call it Kernel ICA or KICA. In the experiments, the polynomial kernel is used, and experimental results show the proposed method has an encouraging performance.