The nature of statistical learning theory
The nature of statistical learning theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
Support vector machines applied to face recognition
Proceedings of the 1998 conference on Advances in neural information processing systems II
Local feature analysis: a statistical theory for information representation and transmission
Local feature analysis: a statistical theory for information representation and transmission
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Recent advances in visual and infrared face recognition: a review
Computer Vision and Image Understanding
Journal of Cognitive Neuroscience
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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This paper presents a robust method for recognizing human faces under varying illuminations. Unlike conventional approaches for recognizing faces in the spatial domain, we model the phase information of face images in the frequency domain and use them as features to represent faces. Then, Support Vector Machines (SVM) are applied to claim an identity using different kernel methods. Due to large variations of the face images, algorithms which perform in the space domain need more training images to achieve reasonable performance. On the other hand, the SVM combined with the phase-only representation of faces performs well even with small number of training images. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and 3D Linear Subspace (3DLS) are included in the experiment changing the size of images and the number of training images in order to find the best parameters associated with each method. The illumination subset of the CMU-PIE database is used for the performance evaluation.