基于DWT和PCA及LDA的人耳识别

根据主成分分析(PCA)和线性判别分析(LDA)在人耳识别过程中存在识别率不高的问题,提出一种基于离散小波变换(DWT)和PCA及LDA的人耳识别算法。将人耳图像进行二维DWT,选择包含图像大部分信息的低频子带,先利用PCA再利用LDA提取最优样本映射空间,最后利用最近邻法则进行人耳图像的分类。实验结果表明,该方法识别效果优于基于PCA及LDA的方法。

Ear recognition based on DWT,PCA and LDA

According to the principal component analysis (PCA) and linear discriminant analysis (LDA) identification rate is not high in ear recognition process, ear recognition algorithm based on discrete wavelet transform (DWT), PCA and LDA is proposed. First, decompose the human ear image with two-dimensional DWT and select low-frequency sub-band that contains the main image information, and then use PCA and LDA in succession to extract the optimal sample mapping space, finally, use the nearest neighbor rule to classify the human ear image. The experimental results show that this method is superior to identify method of PCA and LDA.