基于小波矩融合PCA变换的手指静脉识别

Finger Vein Recognition Based on Wavelet Moment Fused with PCA Transform

手指静脉识别作为一种新的身份认证技术相对于其他生物特征识别技术有很多优点,有着很广阔的应用前景.本文提出一种基于小波矩融合 PCA 变换和 LDA 变换的算法,不仅克服了单一特征识别识别率不高的缺点,而且也解决了普通的模板匹配的速度问题.实验结果表明,本文方法能够快速准确地进行身份识别,效果较令人满意.

As a new kind of identity authentication technology, finger vein recognition has more merits than other biometric feature authentication system. Therefore, it has a vast application prospect. The algorithm based on wavelet combined with Principal Component Analysis （PCA） transformation and LDA transformation is proposed. It not only overcomes the disadvantage of the single feature recognition, but also solves the low-speed problem of common template matching. Experimental results indicate that the proposed method can provide fast and accurate identification, and the results are satisfactory.