A BEMD based normalization method for face recognition under variable illuminations

Face recognition remains challenging in computer vision due to variations on face, especially for illuminations. In this paper, a novel face illumination normalization method is proposed. By using Bidimensional Empirical Mode Decomposition (BEMD), a series of normalization images (BIMF) from one subject can be extracted with different spatial scales, each of which possesses a high recognition rate compared with former representative methods, i.e., SQI, LOG-DCT and LTV. What´s more, canonical correlation analysis (CCA) is adopted in this paper to combine images generated from one input to form more discrimative features. Experiments on Yale B, Extended Yale B and CMU PIE show that the proposed method, though simple, is very effective when dealing with face recognition under variable lighting conditions.