Composite sketch recognition using saliency and attribute feedback

Recent interest and requirement of law enforcement agencies in matching composite sketches with digital images has instigated research in this important face recognition problem. In this paper, we propose feature extraction and matching algorithm using visual saliency and combination of texture features for matching composite sketches with digital photos. The attributes such as gender, ethnicity, and skin color are utilized for re-ordering the ranked list. Further, information from multiple experts such as multiple composite sketch generation tools or artists is combined for improving the matching performance. The results obtained on the extended PRIP database show that the proposed algorithm improves the state-of-art in matching composite sketch and digital face images and yields the rank 50 identification accuracy of 70.3% on a database of 1500 subjects.