从噪音到地物:利用强度分布作为新软生物测量轨迹来识别手指肉 (From Noise to Feature: Exploiting Intensity Distribution as a Novel Soft Biometric Trait for Finger Vein Recognition)

Most finger vein feature extraction algorithms achieve satisfactory performance due to their texture representation abilities, despite simultaneously ignoring the intensity distribution that is formed by the finger tissue, and in some cases, processing it as background noise. In this paper, we exploit this kind of noise as a novel soft biometric trait for achieving better finger vein recognition performance. First, a detailed analysis of the finger vein imaging principle and the characteristics of the image are presented to show that the intensity distribution that is formed by the finger tissue in the background can be extracted as a soft biometric trait for recognition. Then, two finger vein background layer extraction algorithms and three soft biometric trait extraction algorithms are proposed for intensity distribution feature extraction. Finally, a hybrid matching strategy is proposed to solve the issue of dimension difference between the primary and soft biometric traits on the score level. A series of rigorous contrast experiments on three open-access databases demonstrates that our proposed method is feasible and effective for finger vein recognition.

翻译：多数手指血管特征提取算法由于其纹理表达能力而取得令人满意的性能,尽管它们同时忽略了手指组织形成的强度分布,在某些情况下,它被加工成背景噪音。在本文件中,我们利用这种噪音作为新的软生物鉴别特征,以取得更好的手指血管识别性性能。首先,对手指血管成像原理和图像特征进行详细分析,以表明在背景中由手指组织形成的强度分布可作为软生物鉴别特征进行提取。然后,为强度分布特征提取建议了两个手指血管背景提取算法和三个软生物鉴别特征提取算法。最后,建议采用混合匹配战略,以解决得分水平上初级生物鉴别特征与软生物鉴别特征之间的维度差异问题。对三个开放数据库的一系列严格对比实验表明,我们提议的方法对于切口识别是可行和有效的。