Zhang Baoqing, Mu Zhichun, Zeng Hui, Luo Shuang
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
ScientificWorldJournal. 2014 Feb 24;2014:131605. doi: 10.1155/2014/131605. eCollection 2014.
Orientation information is critical to the accuracy of ear recognition systems. In this paper, a new feature extraction approach is investigated for ear recognition by using orientation information of Gabor wavelets. The proposed Gabor orientation feature can not only avoid too much redundancy in conventional Gabor feature but also tend to extract more precise orientation information of the ear shape contours. Then, Gabor orientation feature based nonnegative sparse representation classification (Gabor orientation + NSRC) is proposed for ear recognition. Compared with SRC in which the sparse coding coefficients can be negative, the nonnegativity of NSRC conforms to the intuitive notion of combining parts to form a whole and therefore is more consistent with the biological modeling of visual data. Additionally, the use of Gabor orientation features increases the discriminative power of NSRC. Extensive experimental results show that the proposed Gabor orientation feature based nonnegative sparse representation classification paradigm achieves much better recognition performance and is found to be more robust to challenging problems such as pose changes, illumination variations, and ear partial occlusion in real-world applications.
方向信息对于耳部识别系统的准确性至关重要。本文研究了一种利用Gabor小波方向信息进行耳部识别的新特征提取方法。所提出的Gabor方向特征不仅可以避免传统Gabor特征中过多的冗余，而且倾向于提取耳部形状轮廓更精确的方向信息。然后，提出了基于Gabor方向特征的非负稀疏表示分类（Gabor方向+NSRC）用于耳部识别。与稀疏编码系数可以为负的SRC相比，NSRC的非负性符合将部分组合成整体的直观概念，因此更符合视觉数据的生物建模。此外，Gabor方向特征的使用增加了NSRC的判别能力。大量实验结果表明，所提出的基于Gabor方向特征的非负稀疏表示分类范式取得了更好的识别性能，并且发现在实际应用中对诸如姿态变化、光照变化和耳部部分遮挡等具有挑战性的问题更具鲁棒性。
