1 research outputs found
Scoot: A Perceptual Metric for Facial Sketches
Human visual system has the strong ability to quick assess the perceptual
similarity between two facial sketches. However, existing two widely-used
facial sketch metrics, e.g., FSIM and SSIM fail to address this perceptual
similarity in this field. Recent study in facial modeling area has verified
that the inclusion of both structure and texture has a significant positive
benefit for face sketch synthesis (FSS). But which statistics are more
important, and are helpful 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
demonstrate that our Scoot metric exceeds the performance of prior work.
Besides, we built the first large scale (152k judgments) human-perception-based
sketch database that can evaluate how well a metric is consistent with human
perception. Our results suggest that "spatial structure" and "co-occurrence
texture" are two generally applicable perceptual features in face sketch
synthesis.Comment: Code & dataset:http://mmcheng.net/scoot/, 11 pages, ICCV 2019, First
one good evaluation metric for facial sketh that consistent with human
judgment. arXiv admin note: text overlap with arXiv:1804.0297