基於身份超解析之卷積類神經網路的人臉超解析

Abstract

人臉超解析是一個生成任務，它目標是對低解析度的人臉超解析。而人在觀察一個人臉的時候會下意識地著重在人臉的身份資訊上。然而現有的人臉超解析方法都忽略了身份資訊的恢復。本論文提出身份超解析網路來對身份資訊進行恢復。特別地，我們定義了一個身份超解析損失函數來衡量超解析人臉和高解析度人臉的身份資訊的差異。這種差異的度量是在超球度量空間上進行。然而，直接使用這個損失函數會導致一個動態的域分叉問題。這是由於超解析度域和真實高解度域之間存在很大的差距。為了克服這個問題，我們提出了域整合訓練方法。它可以為這兩個不同的域建立一個魯棒的身份度量空間。通過充分的實驗驗證，我們的方法在放大12x14像素的人臉八倍的時候比其它方法取得更好的視覺效果。此外，我們的方法顯著地提升了低分辨度人臉的可辨識能度。

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 domain-integrated training approach by constructing a robust identity metric for faces from these two domains. Extensive experimental evaluations demonstrate that the proposed SICNN achieves superior hallucination visual quality over the state-of-the-art methods on a challenging task to super-resolve 12x14 faces with an 8x upscaling factor. In addition, SICNN significantly improves the recognizability of ultra-low-resolution faces.