Scoot: A Perceptual Metric for Facial Sketches

Deng-Ping Fan, ShengChuan Zhang, Yu-Huan Wu, Yun Liu, Ming-Ming Cheng, Bo Ren, Paul L. Rosin, Rongrong Ji; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 5612-5622


While it is trivial for humans to quickly assess the perceptual similarity between two images, the underlying mechanism are thought to be quite complex. Despite this, the most widely adopted perceptual metrics today, such as SSIM and FSIM, are simple, shallow functions, and fail to consider many factors of human perception. Recently, the facial modeling community has observed that the inclusion of both structure and texture has a significant positive benefit for face sketch synthesis (FSS). But how perceptual are these so-called "perceptual features"? Which elements are critical for their success? In this paper, we design a perceptual metric, called Structure Co-Occurrence Texture (Scoot), which simultaneously considers the block-level spatial structure and co-occurrence texture statistics. To test the quality of metrics, we propose three novel meta-measures based on various reliable properties. Extensive experiments verify that our Scoot metric exceeds the performance of prior work. Besides, we built the first largest scale (152k judgments) human-perception-based sketch database that can evaluate how well a metric consistent with human perception. Our results suggest that "spatial structure" and "co-occurrence texture" are two generally applicable perceptual features in face sketch synthesis.

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author = {Fan, Deng-Ping and Zhang, ShengChuan and Wu, Yu-Huan and Liu, Yun and Cheng, Ming-Ming and Ren, Bo and Rosin, Paul L. and Ji, Rongrong},
title = {Scoot: A Perceptual Metric for Facial Sketches},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}