Facial composites are widely used by law enforcement agencies to assist in the identification and apprehension of suspects involved in criminal activities. These composites, generated from witness descriptions, are posted in public places and in the media with the hope that some viewers will provide tips about the identity of the suspect. This legacy method of identifying suspects is slow, tedious, and may not even lead to the timely apprehension of the suspect. Hence, there is a need for a method that can automatically and efficiently match facial composites to large police mugshot databases. As a result of this requirement, facial composite recognition is an important topic for biometrics researchers. While substantial progress has been made in non-forensic facial composite (or viewed composite) recognition over the past decade, very little work has been done using operational composites relevant to law enforcement agencies. Furthermore, to our knowledge, no facial composite to mugshot matching system has been documented that is readily deployable. The contributions of this thesis include: (i) an exploration of composite recognition use cases involving multiple forms of facial composites, (ii) the FaceSketchID System, a scalable and operationally deployable software system that achieves state-of-the-art matching accuracy on facial composites using two complementary algorithms (holistic and component-based), and (iii) a study of the effects of training data on algorithm performance. Experimental results are presented using a large mugshot gallery that is representative of a law enforcement agency's mugshot database. All results are compared against three state-of-the-art commercial-off-the-shelf (COTS) face recognition systems.
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