Data augmentation for face recognition

Recently, Deep Convolution Neural Networks (DCNNs) have shown outstanding performance in face recognition. However, the supervised training process of DCNN requires a large number of labeled samples which are expensive and time consuming to collect. In this paper, we propose five data augmentation methods dedicated to face images, including landmark perturbation and four synthesis methods (hairstyles, glasses, poses, illuminations). The proposed methods effectively enlarge the training dataset, which alleviates the impacts of misalignment, pose variance, illumination changes and partial occlusions, as well as the overfitting during training. The performance of each data augmentation method is tested on the Multi-PIE database. Furthermore, comparison of these methods are conducted on LFW, YTF and IJB-A databases. Experimental results show that our proposed methods can greatly improve the face recognition performance.