Author(s): Sun, LJ (Sun, Linjun); Li, WJ (Li, Weijun); Ning, X (Ning, Xin); Zhang, LP (Zhang, Liping); Dong, XL (Dong, Xiaoli); He, W (He, Wei)

Source: IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS Volume: E103D Issue: 5 Pages: 1185-1189 DOI: 10.1587/transinf.2019EDL8103 Published: MAY 2020

Abstract: This letter proposes a gradient-enhanced softmax supervisor for face recognition (FR) based on a deep convolutional neural network (DCNN). The proposed supervisor conducts the constant-normalized cosine to obtain the score for each class using a combination of the intra-class score and the soft maximum of the inter-class scores as the objective function. This mitigates the vanishing gradient problem in the conventional softmax classifier. The experiments on the public Labeled Faces in the Wild (LFW) database denote that the proposed supervisor achieves better results when compared with those achieved using the current state-of-the-art softmax-based approaches for FR.

Accession Number: WOS:000530668200032

ISSN: 1745-1361

Full Text: https://www.jstage.jst.go.jp/article/transinf/E103.D/5/E103.D_2019EDL8103/_article