Unsupervised facial image occlusion detection with deep autoencoder

Face recognition techniques have been developed significantly in recent years. However, recognizing faces with partial occlusion is still a challenging problem. Although there are many works to solve the problem of obscuring the face, the occlusion is still a challenge in face recognition. To overcome this issue, firstly we should detect the occlusion position in the facial images. We construct a robust self-encoding machine to solve the occlusion detection problem in face images and uses synthetic occlusion data for training. We evaluated our method under various synthetic occlusion face images. Experiments show that our method can effectively detect various types of occlusion masks in an unsupervised manner and has better robustness to the occlusion categories.