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%.
Highlights
Recent studies have shown that deep neural networks perform well in face detection [1], [2], face alignment [3], and face verification [4], [5]
We evaluated the effectiveness of the proposed method on a series of benchmark datasets including Labeled Faces in the Wild (LFW), CASIA-WebFace
The experimental results are compared with other methods on Similar Face Dataset (SFD)
Summary
Recent studies have shown that deep neural networks perform well in face detection [1], [2], face alignment [3], and face verification [4], [5]. One of the most important ingredients to the success of such methods is the availability of large quantities of training data. The most recent face recognition method of Google [6] was trained using 200 million images and 8 million unique identities. There are still some problems with the above method in similar face recognition. Due to the lack of public similar face dataset in academia, it has become very difficult to research. Needless to say, building a dataset this large is beyond the capabilities of most international research groups, in academia
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