ArcVein-Arccosine Center Loss for Finger Vein Verification
Borui Hou, Ruqiang Yan
Abstract
This article proposes a new loss function termed arccosine center loss, which can learn interclass and intraclass information simultaneously, to improve the discriminative ability of convolutional neural networks for finger vein verification. Specifically, the purpose of arccosine center loss is to reduce intraclass distance and increase the interclass distance through network model training. With the combination of softmax loss and arccosine center loss, the proposed network model can extract features with the interclass dispensation and intraclass compactness, which improves the discriminative ability of learned features. Experimental studies have confirmed the effectiveness and efficiency of the proposed method for finger vein verification.