Similar Face Recognition Using the IE-CNN Model
Anping Song, Qian Hu, Xuehai Ding, Xinyi Di, Zi-Heng Song
Abstract
In the field of face recognition, similar face recognition is difficult due to the high degree of similarity of the face structure. The following two factors are needed to make progress in this field: (i) the availability of large scale similar face training datasets, and (ii) a fine-grained face recognition method. With the above factors fulfilled, we make two contributions. First, we show how a large scale similar face dataset (SFD) can be assembled by a combination of automation and human in the loop, and divide the dataset into five grades according to different degrees of similarity. Second, a new fine-grained face feature extraction method is proposed to solve this problem using the attention mechanism which combines the Internal Features and External Features. The Labeled Faces in the Wild (LFW) database, CASIA-WebFace and similar face dataset (SFD) were selected for experiments. It turns out that the true positive rate is improved by 1.94 - 5.66% and the recognition accuracy rate improved by 2.08 - 5.8% for the LFW and CASIA-WebFace database, respectively. Meanwhile for SFD, the recognition accuracy rate improved by 18.80 - 35.84%.