Scoot: A Perceptual Metric for Facial Sketches

Deng-Ping Fan, ShengChuan Zhang, Yu-Huan Wu, Yun Liu, Ming-Ming Cheng, Bo Ren, Rosin Paul L, Rongrong Ji

Abstract: The human visual system has a strong ability to quickly assess the perceptual similarity between two facial sketches. However, existing popular facial sketch metrics, e.g., FSIM and SSIM, which initially designed for evaluating local image distortion, often fail to address the perceptual similarity between faces. 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.