Texture analysis for ear recognition using local feature descriptor and transform filter
Jun Ying Feng, Zhichun Mu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Ear recognition is a kind of the novel representative subjects in the field of non-disturbance biometrics authentication and is becoming received wide attention in academic research. In this paper, the ear recognition problem based on texture analysis is discussed. A novel local wavelet binary pattern descriptor combining local binary pattern descriptor with wavelet transform filter is presented. And an ear recognition approach based on local wavelet binary pattern descriptor and support vector machines classification is proposed, which is tested on USTB ear image set. The experiment results show that the ear recognition scheme using local feature descriptor and transform filter is effective and promising. The performance of support vector machines classifier is better than that of K Nearest Neighbor classifier. The best combination occurs under the Chi square distance and 'reverse biorthogonal 3.1' wavelet, and the 96.86% cross- validation recognition rate is obtained.