基于最佳局部差值编码位的手指静脉识别  

Finger Vein Recognition Based on Best Local Difference Code Bit

作　　者：袭肖明 尹义龙[3] 张梦羽 杨璐 孟宪静 杜亨方 

机构地区：[1]山东财经大学计算机科学与技术学院,济南250014 [2]山东财经大学山东省数字媒体重点实验室,济南250014 [3]山东大学计算机科学与技术学院,济南250101

出　　处：《模式识别与人工智能》2017年第9期850-858,共9页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61701280;61703235;61671274;6157321);山东省自然科学基金项目(No.ZR2016FQ18);山东省高等学校优势学科人才团队培育计划资助~~

摘　　要：针对手指静脉识别技术中现有编码特征局部细节信息和区分性信息利用不充分的问题,文中提出基于最佳局部差值编码位(BLDCB)的手指静脉识别方法.设计局部差值编码提取方法获取图像的编码特征,提出基于相关性和散度的最佳位挖掘方法.引入条件概率计算编码位与目标的相关性,挖掘鲁棒位.计算鲁棒位之间的类间散度,获取具有区分性的鲁棒位,作为最佳位.在公开的Ploy U手指静脉数据库和MLA手指静脉数据库上的实验验证文中方法可以进一步提高身份验证的精度和速度.In finger vein recognition, the local details of the existing code based features are ignored and the discrimination information cannot be fully used. To solve these problems, a best local difference code bit （BLDCB） method is proposed for finger vein recognition. The local difference code （LDC） extraction method is developed to extract the codes. Then, the best bit mining criterion based on relation and inter-class divergence is designed for mining best bits in extracted codes. The conditional probability is calculated for capturing relation between bits and subjects and mining robust bits. Consequently, the intra-class divergence is used for mining discriminative bits from the robust bits, and the selected bits are used as the best bits. Therefore, the best bits are more robust and discriminative. The experimental results on PloyU database and self-constructed finger vein database demonstrate the effectiveness and efficiency of the proposed method.

关 键 词：手指静脉识别 局部差值编码 最佳局部差值编码位(BLDCB) 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]