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.

本文提出了一种在嵌入空间中提高人脸可识别性的方法，该方法基于两个标准：每个面嵌入与不可识别面群集中心的接近程度以及每个面嵌入与其正面和负面类原型的接近程度，并引入了感知注意机制来关注最具识别性的面部区域。经过对三个具有挑战性的低分辨率数据集和面部质量评估进行了广泛的评估，证明了所提出的模型在 VLRFR 方面的优越性。