Face Recognition using contourlet Transform and Multidirectional Illumination from a Computer Screen

Images of a face under arbitrary distant point light source illuminations can be used to construct its illumination cone or a linear subspace that represents the set of facial images under all possible illuminations. However, such images are difficult to acquire in everyday life due to limitations of space and light intensity. This paper presents an algorithm for face recognition using multidirectional illumination generated by close and extended light sources, such as the computer screen. The Contourlet coefficients of training faces at multiple scales and orientations are calculated and projected separately to PCA subspaces and stacked to form feature vectors. These vectors are projected once again to a linear subspace and used for classification. During testing, similar features are calculated for a query face and matched with the training data to find its identity. Experiments were performed using in house data comprising 4347 images of 106 subjects and promising results were achieved. The proposed algorithm was also tested on the extended Yale B and CMU-PIE databases for comparison of results to existing techniques.