Data insufficiency in sketch versus photo face recognition
Jonghyun Choi, Abhishek Sharma, David W. C. Jacobs, Larry Steven Davis · 2012
Computerized sketch-face recognition is a crucial element for law enforcement and has received considerable attention in the recent literature. Sketches of the suspect are hand-drawn or computer-rendered based on a verbal description of the suspect. However, the most popular and the only publicly available dataset, i.e. the CUFS face-sketch dataset, is far from realistic because the sketches are hand-drawn with the artist looking at the photographs to be matched later. After years of effort, researchers are producing nearly perfect results. However, we show that this is not because the problem is solved, but because of flaws in the dataset. In this paper, we empirically show that an off-the-shelf face recognition system for photo-sketch and sketch-photo matching with simple shape and edge features outperforms more sophisticated state-of-the-art approaches even without using training data. We additionally show that just using the hair region gives a 85.22% recognition rate. Based on the empirical evidences we argue that the current dataset available for face-sketch matching purposes is not appropriate and needs to be replaced by a more realistic one for advancement of this field.