  Existing face sketch synthesis (FSS) similarity measures are sensitive to
slight image degradation (e.g., noise, blur). However, human perception of the
similarity of two sketches will consider both structure and texture as
essential factors and is not sensitive to slight ("pixel-level") mismatches.
Consequently, the use of existing similarity measures can lead to better
algorithms receiving a lower score than worse algorithms. This unreliable
evaluation has significantly hindered the development of the FSS field. To
solve this problem, we propose a novel and robust style similarity measure
called Scoot-measure (Structure CO-Occurrence Texture Measure), which
simultaneously evaluates "block-level" spatial structure and co-occurrence
texture statistics. In addition, we further propose 4 new meta-measures and
create 2 new datasets to perform a comprehensive evaluation of several
widely-used FSS measures on two large databases. Experimental results
demonstrate that our measure not only provides a reliable evaluation but also
achieves significantly improved performance. Specifically, the study indicated
a higher degree (78.8%) of correlation between our measure and human judgment
than the best prior measure (58.6%). Our code will be made available.
