摘要
本文研究了独立成分分析(ICA)两种不同的结构ICAⅠ和ICAⅡ在掌纹识别中的应用。为了提高识别准确性和可靠性,该方法首先对掌纹图像进行预处理,提取掌纹感兴趣(ROI)区域进行特征提取和匹配。为了减少计算量,运用ICA算法之前,先采用主成分分析(PCA)算法去除掌纹图像的二阶统计特征相关性,其余的高阶统计特征由ICA分离。对于PolyU掌纹图像库,基于ICA模型的预测误差平方和(SPE)小于PCA,而且重构的原始图像优于PCA。为了比较两种算法识别性能,本文分别用PCA、ICAⅠ、ICAⅡ提取特征掌纹子空间,然后将待识别图像投影到低维子空间上,最后用余弦距离进行掌纹匹配。实验结果表明,ICA算法两种结构的识别率均高于PCA,ICAⅡ在性能上优于ICAⅠ。
Two different architectures of Independent Component Analysis （ICA） to palmprint recognition which were architecture Ⅰ and architecture Ⅱ were discussed. Region of Interest （ROI） in the palmprint images were extracted automatically by preprocessing before feature extraction and match so as to increase the recognition accuracy and reliability. In order to reduce computational complexity, Principal Component Analysis （PCA） was used to eliminate second-order dependencies in the palmprint images. The remaining higher-order dependencies were separated by ICA. The Square Project Error （SPE） of ICA model was smaller than that of PCA, and the reconstruction of the original palmprint images was superior to that gotten by PCA in PolyU Palmprint Database. To compare the recognition performance of two ICA architectures with PCA, we applied them to extract the palmprint feature subspace inside ROI. Then the images to be recognized were projected on small dimension subspace. Finally, we used a classifier to palmprint match based on cosine distance. Experimental results show that two ICA architectures perform better than PCA and ICA architecture Ⅱ is the best in performance.
出处
《光电工程》
CAS
CSCD
北大核心
2008年第3期136-139,共4页
Opto-Electronic Engineering
关键词
图像预处理
主成分分析
独立成分分析
掌纹识别
余弦距离
image preprocessing
principal component analysis （PCA）
independent component analysis （ICA）
palmprint recognition
cosine distance
