融合小波变化和张量PCA的人耳识别算法

摘要

Comprehensively utilizing the advantages of wavelet transform and TPCA, this paper proposed a new recognition algorithm of the human ear, to the human ear images used wavelet transform to do the first pre-processing to be four sub-band image, and then for each sub-band images used tensor PCA feature extraction to achieve efficient human ear image recognition.Simulation results show that comparing this method and only the principal component analysis identified tensor, it improves the recognition rate.%综合利用小波变换和张量主成分分析这两个算法的优点,提出了一种新的人耳识别方法,对人耳图像先采用小波变换作预处理得到四个子带图像;然后对每个子带图像用张量PCA进行特征提取;最后利用最近邻的方法实现人耳图像识别.实验结果表明,利用此方法与只用主成分分析识别相比,提高了识别率.