Low-Resolution Face Recognition Based on Identity-Preserved Face Hallucination
Shun-Cheung Lai, Chenhang He, Kin‐Man Lam · 2019
The state-of-the-art Convolutional Neural Network (CNN)-based methods have achieved promising recognition performance on human face images. However, the accuracy cannot be retained when face images are at very low resolution (LR). In this paper, we propose a novel loss function, called identity-preserved loss, which combines with the image-content loss to jointly supervise CNNs, for performing face hallucination and recognition simultaneously. Therefore, the trained network is able to perform face hallucination and identity preservation, even if the query face is of very low resolution. More importantly, experimental results show that our proposed method can preserve the identities for the LR images from unknown subjects, who are not included in the training set. The source code of our proposed method is available at: https://github.com/johnnysclai/SR_LRFR.