Finger Vein Recognition and Intra-Subject Similarity Evaluation of Finger Veins using the CNN Triplet Loss

Finger vein recognition deals with the identification of subjects based on their venous pattern within the fingers. There is a lot of prior work using hand crafted features, but only little work using CNN based recognition systems. This article proposes a new approach using CNNs that utilizes the triplet loss function together with hard triplet online selection for finger vein recognition. The CNNs are used for three different use cases: (1) the classical recognition use case, where every finger of a subject is considered as a separate class, (2) an evaluation of the similarity of left and right hand fingers from the same subject and (3) an evaluation of the similarity of different fingers of the same subject. The results show that the proposed nets achieve superior results compared to prior work on finger vein recognition using the triplet loss function. Furtherly, we show that different fingers of the same subject, especially symmetric fingers (same finger type but from different hand), show enough similarities to perform recognition. The last statement contradicts the current understanding in the literature for finger vein biometry, in which it is assumed that different fingers of the same subject are unique identities.