Complex Background Palm Segmentation using SVM

An efficient method is proposed to enhance complex background segmentation for preprocessing in palmprint recognition. In this paper, we integrate texture and haze features by applying Laws masks to the image represented in YCbCr color space, and use these features of a patch with its neighborhood information to determine hand and non-hand regions by support vector machine (SVM). Compared with other methods, our algorithm demonstrated robustness to changing illumination and a complex environment and we obtain a relatively clear hand shape with an average accuracy of 94.96%. The images in our experiments are taken with popular mobile phones in our laboratory.