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Scoot: A perceptual metric for facial sketches

Fan, Deng-Ping, Zhang, ShengChuan, Wu, Yu-Huan, Liu, Yun, Cheng, Ming-Ming, Ren, Bo, Rosin, Paul ORCID: and Ji, Rongrong 2019. Scoot: A perceptual metric for facial sketches. Presented at: ICCV 2019: International Conference on Computer Vision, Seoul, South Korea, 27 Oct - 2 Nov 2019. IEEE International Conference on Computer Vision. Computer Vision Foundation, pp. 5612-5622.

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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.

Item Type: Conference or Workshop Item (Paper)
Date Type: Completion
Status: Published
Schools: Computer Science & Informatics
Publisher: Computer Vision Foundation
Related URLs:
Date of First Compliant Deposit: 20 December 2019
Date of Acceptance: 26 July 2019
Last Modified: 26 Oct 2022 08:17

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