Scoot: A Perceptual Metric for Facial Sketches

TL;DR

Scoot is a new perceptual metric for facial sketches that considers structure and texture, outperforming existing metrics and aligning well with human perception, supported by a large-scale human judgment database.

Contribution

Introduces Scoot, a novel perceptual metric combining structure and texture, and establishes a large-scale human perception database for facial sketch quality evaluation.

Findings

Scoot outperforms existing metrics like FSIM and SSIM.

Spatial structure and co-occurrence texture are key perceptual features.

A large-scale human judgment database validates the metric's effectiveness.

Abstract

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.