大样本手背静脉身份识别的关键问题研究

本项目主要研究大样本手背静脉身份识别的关键问题。针对大样本应用的实际问题,凝练三个具体科学问题:样本集规模、图像质量对识别精度影响的理论分析，多人种、老龄化和非线性形变的鲁棒特征提取问题和大规模数据库实时识别问题；具体研究内容为：特征可分性与大样本集容量的研究，图像质量参数与识别率关系的研究，手背静脉多光谱特性的活体检测研究，对皮肤弹性形变鲁棒的特征提取及特征编码研究，多信息融合的模板生成及基于相关处理的分类器研究；研究方案为：进行图像质量参数对识别率影响的理论与实验研究，获得图像质量参数的最低要求；分析手背静脉的多光谱特性，提取双色近红外峰值比研究手背静脉图像的活体检测；对手背静脉特征进行纠错编码提高类间距离增加特征鲁棒性，构造对手背静脉纹理结构特征鲁棒的码字典以减小类内距离，保证高的识别率；面向实际应用，采用匹配压缩快速运算方法，一定程度上解决识别精度与识别速度相互制约的问题。

The main purpose of this project is to find solutions of some critical issues in the research of the hand dorsal vein identification by large-scale samples. According to the practical use of large-scale sample hand vein recognition, the critical issues can be defined as three scientific ones: theoretical analysis of how the hand vein sample scale and image quality may affect the recognition accuracy; analysis of the robustness when doing vein features extraction of different races, aging and the non-linear deformation; analysis of real-time recognition in large sample data base. The content of the research is as follows: analyzing the recognizable feature and the capacity of large-scale samples; finding out how the hand vein image quality may affect the recognition rate; considering liveness detection based on the optical properties of hand vein in the rich NIR spectrum; extracting the vein feature in consideration of robustness against the elastic deformation; generating templates of multi-information fusion and devising classifier based on the feature similarity analysis. The research approach is as follows: to obtain the minimum parameter of ensuring hand vein image quality by theoretical analysis and experimental study on how the recognition rate is affected by image quality; to test liveness detection by analyzing characters of hand dorsal vein in the multi-spectrum and calculating its peak rate value; to enhance robustness by error correction coding; to increase recognition rate by strengthening the distinguishability of different classes and the similarity of the same class, with building code dictionary of hand dorsal vein feature; for practical applications, one fast compute compression-matching algorithm is adopted to solve the interacting problem with recognition accuracy and recognition speed.