Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
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
An efficient illumination normalization method for face recognition
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Selection and fusion of facial features for face recognition
Expert Systems with Applications: An International Journal
Fusion of classifiers for illumination robust face recognition
Expert Systems with Applications: An International Journal
Modular image principal component analysis for face recognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Evaluation of face recognition techniques using PCA, wavelets and SVM
Expert Systems with Applications: An International Journal
On image matrix based feature extraction algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper proposes two feature extraction techniques that minimizes the effects of distortions generated by variations in illumination, rotation and, head pose in automatic face recognition systems. The proposed techniques are Modular IMage Principal Component Analysis (MIMPCA) and weighted Modular Image Principal Component Analysis (wMIMPCA). Both techniques are based on PCA and they use the modular image decomposition to minimize local variation. Also, the covariance matrix is calculated directly from the original image matrix. This strategy generates a smaller matrix compared with traditional PCA and reduces the computational effort. wMIMPCA assumes that parts of the face are more discriminatory than others, so a Genetic Algorithm is used to obtain weights for each region in the face image. The proposed techniques are compared with Modular PCA and two-dimensional PCA using three well-known databases, showing better results.