  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.
