Client Specific Image Gradient Orientation for Unimodal and Multimodal Face Representation

Multimodal face recognition systems usually provide better recognition performance compared to systems based on a single modality. To exploit this advantage, in this paper, an image fusion method which integrates region segmentation and pulse coupled neural network (PCNN) is used to obtain fused images by using visible (VIS) images and infrared (IR) images. Then, client specific image gradient orientation (CSIGO) is proposed which is inspired by the successful application of client specific technique and image gradient orientations technique. As most of the traditional appearance-based subspace learning algorithms are not robust to illumination changes, to remedy this problem to some extent, we adopt the image gradient orientations method. Moreover, to better describe the discrepancies between different classes, client specific is introduced to derive one dimensional Fisher face per client. Thus CS-IGO-LDA and improved CS-IGO-LDA are proposed in this paper, which combine the merits of IGO and client specific technique. Experimental results obtained on publicly available databases indicate the effectiveness of the proposed methods on unimodal and multimodal face recognition.