一种基于2DHOL特征与(2D)2FPCA结合的手指静脉识别方法

摘要

文中提出一种基于二维方向线直方图统计(2DHOL)特征与双向二维费希尔主成分分析((2D)2FPCA))相结合的手指静脉识别方法.首先针对手指静脉图像纹路走向的特点,改进基于梯度直方图(HOG)特征中有关梯度幅值和方向的计算方法,采用二维Gabor滤波器获取静脉图像的线形响应和方向,提取2DHOL特征;然后综合考虑行列相关性和类别信息,采用(2D)2FPCA对2DHOL特征进行降维处理,得到手指静脉特征向量;最后计算特征向量的欧氏距离.应用不同手指静脉数据库进行实验的结果表明,该方法能够有效地提高手指静脉识别率,并对训练样本数变化具有较强的鲁棒性.%A finger vein recognition method based on two-dimensional histogram of oriented lines (2DHOL) and two-directional two-dimensional Fisher principal component analysis ((2D)2FPCA) is proposed in this pa-per. Firstly, according to the characteristics of different finger vein lines, the calculation method of gradient amplitude and direction in the histogram gradient histogram (HOG) is improved. The line responses and orien-tation of pixels in finger vein images are extracted by adopting the real part of 2-D Gabor filter. In this way, 2DHOL features are obtained. Secondly, considering the correlation between columns and rows of a finger vein image, and combining with the image's category information, the dimension of 2DHOL features is reduced by employing (2D)2FPCA. Finally, Euclidean distance is utilized in recognition. Experimental results applying different finger vein image databases show that, the proposed method can effectively improve the accuracy of finger vein recognition, and enhance the robustness to quantity changes of training samples.