Learning Feature Representation for Face Verification

Abstract

Previous models based on Deep Convolutional Neural Networks (DCNN) for face verification focused on learning face representations. The face features extracted from the models are applied to additional metric learning to improve a verification accuracy. The models extract high-dimensional face features to solve a multi-class classification. This results in a dependency of a model on specific training sets since a dimension of the feature should be equal to the number of subjects in a training set. In this paper, we propose a method for learning feature representations which directly determine whether two input images are identical using a single model based on DCNN and residual learning. It is possible to remove the dependency since the model doesn't learn face representations based on multi-class classification. We show that the proposed method achieves the competitive performance for face verification. We demonstrate the face verification performance of the proposed method using the test dataset of Labeled Face in the Wild dataset.