EAR RECOGNITION BASED ON MULTI-SCALE FEATURES

摘要：

This paper proposes a novel ear recognition method using multi-scale features inspired by the theory of the SIFT. Firstly, ear images are normalized by ear outer contour tracking and the longest axis detection. Then the Difference of Gaussian (DOG) images are constructed using scale space theory and their corresponding block-based feature descriptors are determined. Finally we build the nearest neighbor classifiers and EMD is used as the dissimilarity measures. The weighted majority voting technique is used for decision fusion. Compared with other widely used ear recognition methods, such as PCA and KPCA, our method neednt transform the image to the same size and it is more robust to pose and illumination. Extensive experiments have performed to valid its efficiency.

关键词： Ear recognition The difference of Gaussian images Multi-scale feature EMD Decision fusion

作者: HUI ZENG ZHI-CHUN MU LI YUAN

作者单位: School of Information Engineering, University of Science and Technology Beijing, 100083, China

会议类型: 国际会议

会议名称: 2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

会议地点: 保定

会议语种:英文

页码: 2418-2422

在线出版日期: 2009-07-12（万方平台首次上网日期，不代表论文的发表时间）