基于Fisher准则的多指静脉融合

Fusion of Multi-finger Vein Based on Fisher Criterion

针对单特征手指静脉识别中识别率难以继续提高的技术瓶颈,采用多特征融合技术不仅可以提高识别率,而且可以降低误识率.为此提出一种基于Fisher准则的手指静脉融合算法.首先对手指静脉图像进行特征点提取,分别计算待匹配图像特征点与注册图像特征点的正向平均豪斯道夫距离(FMHD)和反向平均豪斯道夫距离(RMHD),然后基于Fisher准则确定FMHD和RMHD的融合参数,将融合得到的豪斯道夫距离作为新的匹配分数;在上述算法的基础上,将得到的食指、中指和无名指3根手指静脉的匹配分数进行融合,以进一步提高手指静脉的识别率.实验结果表明,与通常采用的FMHD相比,采用融合后的豪斯道夫距离的误识率有明显降低;而采用三指静脉融合后,误识率由单个手指的1.95%降低到0.27%.

To improve the single-feature finger vein recognition, multi-feature fusion technology can be utilized not only to improve recognition rate, but also reduce false accept rate. In this paper a new fusion algorithm based on Fisher discriminant criterion is proposed for finger vein recognition. In the proposed algorithm, the forward mean Hausdorff distance （FMHD） and reverse mean Hausdorff distance （RMHD） between the feature points of the enrolled finger vein and those being matched are first computed. Then Fisher criterion is employed to determine the fusion parameters of FMHD and RMHD, with which FMHD and RMHD are fused to generate a new matching score for recognition finger vein. To improve the recognition rate, we further integrate three finger veins （i. e. index finger, middle finger and ring finger） using the proposed new matching score. Experiments demonstrate that the fusion of FMHD and RMHD has a better performance than traditional FMHD matching score, and the integration of three finger veins leads to a significant decrease of false accept when compared to the single finger vein recognition.