Learning hierarchical face representation to enhance HCI among medical robots

In this paper, we propose a hierarchical framework for face recognition by learning deep representation. In order to exploit key patches for face recognition, we separate the entire image into several patches including eyes, nose, and mouth. A binary facegrid is generated to indicate the accurate position of the key patches in face image. The patches are fed into the hierarchical framework to learn the deep representation of the image. We leverage the PCA and SVM method for face recognition. Our face representation can enhance many medical robot applications. Comprehensive experiments have demonstrated that our proposed method can effectively recognize real human faces from fake samples.