基于多维匹配距离融合的指节纹识别

Finger-knuckle-print recognition based on multi-dimensional matching distances fusion

指节纹识别(FKP)作为一种新型的生物特征识别方式,以其安全性和稳定性而备受关注。基于编码的方法被认为是该领域最有成效法之一,在模板匹配阶段通常根据所提取的特征信息计算出2张图片之间的匹配距离来判断样本。然而,一些模糊样本无法通过单一的匹配距离进行有效区分,从而导致较高的错误接受率和错误拒绝率。针对这一问题,提出了一种轻量化且有效的多维匹配距离融合方法。主要思想是基于多种编码方法中不同匹配距离之间的差异性和互补性,利用支持向量机(SVM)对多种匹配距离所构造出的多维特征向量进行分类。其具有极强的通用性,易嵌入到现有的基于编码的方法中。在公开的指节纹数据库PloyU-FKP上进行了从二维到四维匹配距离的大量实验。结果表明,该方法能够普遍提高认证的性能,EER最多可降低22.19%。

As a novel biometric modality, finger-knuckle-print(FKP) recognition has gained much attention for its security and stability. Coding-based methods are considered as one of the most effective methods in this field. Such methods can distinguish samples according to one single matching distance between two images computed from the extracted features in the template matching stage. However, some fuzzy samples cannot be effectively distinguished by one single matching distance, leading to false acceptance and false rejection. To address this problem, a light-weight and effective method based on multi-dimensional matching distances fusion was proposed in this paper.The proposed method utilized the difference and complementarity between different matching distances of multiple coding-based methods, and applied support vector machine(SVM) to the classification of the multi-dimensional feature vectors constructed by the multiple matching distances. What’s more, the proposed method is a general method, which can be easily embedded into the existing coding-based methods. Extensive experiments were conducted for the range from two-dimensional matching distances to four-dimensional matching distances on the public FKP database, PolyU-FKP. The results have shown that the proposed method can generally improve their performances, with a maximum reduction of 22.19% in EER.