Very low-resolution face recognition (VLRFR) poses unique challenges, such as tiny regions of interest and poor resolution due to extreme standoff distance or wide viewing angle of the acquisition devices. In this paper, we study principled approaches to elevate the recognizability of a face in the embedding space instead of the visual quality. We first formulate a robust learning-based face recognizability measure, namely recognizability index (RI), based on two criteria: (i) proximity of each face embedding against the unrecognizable faces cluster center and (ii) closeness of each face embedding against its positive and negative class prototypes. We then devise an index diversion loss to push the hard-to-recognize face embedding with low RI away from unrecognizable faces cluster to boost the RI, which reflects better recognizability. Additionally, a perceptibility attention mechanism is introduced to attend to the most recognizable face regions, which offers better explanatory and discriminative traits for embedding learning. Our proposed model is trained end-to-end and simultaneously serves recognizability-aware embedding learning and face quality estimation. To address VLRFR, our extensive evaluations on three challenging low-resolution datasets and face quality assessment demonstrate the superiority of the proposed model over the state-of-the-art methods.

翻译：极低分辨率人脸识别（VLRFR）面临独特挑战，例如由极端远距离或采集设备广视角导致的目标区域微小、分辨率低下等问题。本文研究在嵌入空间中提升人脸可辨识性（而非视觉质量）的原理性方法。首先，基于两个准则构建鲁棒的基于学习的可辨识度量——可辨识性指数（RI）：(i) 每个人脸嵌入与不可辨识人脸聚类中心的邻近度；(ii) 每个人脸嵌入与其正负类别原型之间的接近程度。随后设计索引散度损失函数，将低RI的难辨识人脸嵌入推离不可辨识人脸聚类，从而提升RI并反映更优可辨识性。此外，引入感知注意力机制以聚焦最具可辨识性的人脸区域，为嵌入学习提供更优的解释性与判别特征。所提模型采用端到端训练，同时服务于可辨识性感知嵌入学习与人脸质量评估。针对VLRFR问题，我们在三个具有挑战性的低分辨率数据集与人脸质量评估上的广泛实验表明，所提模型相较于现有最先进方法具有显著优越性。