Joint Features for Face Recognition under Variable Illuminations

In this paper, we propose a new method using joint features extracted from four efficient face illumination normalization approaches to deal with the face recognition problems under variable lighting conditions. These four methods (Logarithm Total Variation, Generic Intrinsic Illumination Subspace, Self-Quotient Image and Discrete Cosine Transform in Logarithm Domain) can indeed improve recognition rates solely when testing on face database, i.e. Yale B, Extended Yale B and CMU PIE. However, in this paper, we argue that single feature extracted from one method is useful but not adequate to high-accuracy face recognition system. Joint features generated by canonical correlation analysis (CCA) from more than one method can enhance the performance of existing algorithms. It is also suggested that CCA can project different features to the direction that maximize the correlation between them thus leading to an optimized joint feature. Experiments show that our method is not only simple but also effective on promoting face recognition rates.