基于两个方向二维核主成分分析的手指静脉识别

摘要

Finger vein recognition technology, also known as a biometric identify recognition technology, is more superior to fingerprint identification technology and has a broad application prospect. KPCA is a nonlinear feature extraction method, and it overcomes the drawback that linear feature extraction method cannot effectively deal with the higher-order statistics information and nonlinear correlation between pixels. K2DPCA can solve the problem of excessive computation in the one-dimensional matrix operation, but it would require more coefficients for image representation. So the compression effect is far less than the one-dimensional method. Based on KPCA、LDA and MMC,this paper studies the images of the vertical and horizontal directions respectively, obtaining optimal results on the finger vein recognition.%手指静脉识别是一种更优于指纹识别的生物特征识别技术，具有广阔的应用前景。核主成分分析法是一种非线性特征提取方法，克服了线性提取方法未能利用图像中高阶统计信息和多个像素间非线性相关性的缺点。二维核主成分分析法解决了一维操作中出现的矩阵过大导致计算量过大的问题，但却需要更多的系数来表达图像信息，压缩效果远不如一维操作方法。文章基于核主成分分析法，结合线性判决分析法和最大边界准则分析法，对图像的垂直和水平方向分别进行二维分析，使得手指静脉识别取得了最优效果。