Abstract: LLE algorithm is very sensitive to the change of attitude and neighbor points, a novel ear recognition algorithm （Gabor-ILLE） based on Gabor wavelet and improved LLE algorithm is proposed in this paper. The ear features are extracted by Gabor transform, and then the improved LLE is used to reduce dimensionality of features and select the optimal Gabor features of ear recognition, KNN is used to establish the classifier of human ear recognition, and the simulation experiment is carried out on USTB3 ear images. Compared with the reference methods, the proposed algorithm has obtained higher ear recognition rate, and the experimental results verify the effectiveness of the Gabor-ILLE algorithm.

Key words: ear recognition, locally linear embedding, Gabor wavelets, features extraction

摘要： 针对LLE算法对姿态变化和近邻点敏感的缺陷，提了一种融合Gabor小波和改进LLE算法的人耳识别算法（Gabor-ILLE）。该算法通过Gabor变换提取人耳特征，并对Gabor初始特征融合，采用改进LLE对特征进行降维，选择最有利于人耳识别的Gabor特征，采用K近邻算法建立人耳分类器实现人耳识别，并采用USTB3人耳图像库进行仿真实验。相对于参比人耳算法，Gabor-ILLE获得了更高的人耳识别率，实验结果验证了Gabor-ILLE算法的有效性。

关键词: 人耳识别, 局部线性嵌入, Gabor小波, 特征提取