Abstract. Face hallucination is a generative task to super-resolve the
facial image with low resolution while human perception of face heavily relies on identity information. However, previous face hallucination
approaches largely ignore facial identity recovery. This paper proposes
Super-Identity Convolutional Neural Network (SICNN) to recover identity information for generating faces closed to the real identity. Specifically, we define a super-identity loss to measure the identity difference
between a hallucinated face and its corresponding high-resolution face
within the hypersphere identity metric space. However, directly using
this loss will lead to a Dynamic Domain Divergence problem, which is
caused by the large margin between the high-resolution domain and the
hallucination domain. To overcome this challenge, we present a domainintegrated training approach by constructing a robust identity metric
for faces from these two domains. Extensive experimental evaluations
demonstrate that the proposed SICNN achieves superior visual quality
over the state-of-the-art methods on a challenging task to super-resolve
12×14 faces with an 8× upscaling factor. In addition, SICNN significantly improves the recognizability of ultra-low-resolution faces