Recognition of face profiles from the MUGSHOT database using a hybrid connectionist/HMM approach

Biometrical systems have been the focus of concentrated research efforts in recent years. These systems can be used to identify a person or to grant a person access to something, e.g. a room. Face recognition technology has reached a level of performance at which frontal-view recognition of faces with slightly different facial expressions, view angles or head poses can be considered nearly solved. In this paper we present a novel hybrid ANN/HMM approach to recognize a person from that person's profile view (90°) although the recognition system is trained with only one single frontal view of the person. Such a system can be useful for mugshot identification where a victim or witness has seen the criminal from the side only. Our approach uses neural methods in order to synthesize a profile out of the frontal view using no additional knowledge about the 3D shape and structure of a human head. The classification of the generated images is accomplished using a statistical HMM-approach.