Illumination tolerant face recognition using phase-only support vector machines in the frequency domain

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.