Face hallucination by deep traversal network

Zhan-Xiang Feng, Jian-Huang Lai, Xiaohua Xie, Dakun Yang, Ling Mei

Abstract: In this paper, we propose a novel patch-based face hallucination method that consists of two patch-based sparse autoencoder (SAE) networks and a deep fully connected network (namely traversal network). The SAE networks are used to capture the intrinsic features of low-resolution (LR) images and high-resolution (HR) images in the hidden layers, while the traversal network is used to map features from the LR hidden layer to the HR hidden layer. In the training stage, these three networks are jointly optimized. Compared with previous network-based methods that learn an end-to-end mapping from LR images to HR images, our method learns the mapping between hidden layers, which can better alleviate the over-fitting problem. Experimental results demonstrate that our method is efficient and robust for hallucinating face images from both lab environment and the wild. The proposal achieves state-of-the-art performance when conducting face hallucination in CAS-PEAL-R1 database, CMU-PIE database and Casia database.