Human skull identification is an arduous task, traditionally requiring the expertise of forensic artists and anthropologists. This paper is an effort to automate the process of matching skull images to digital face images, thereby establishing an identity of the skeletal remains. In order to achieve this, a novel Shared Transform Model is proposed for learning discriminative representations. The model learns robust features while reducing the intra-class variations between skulls and digital face images. Such a model can assist law enforcement agencies by speeding up the process of skull identification, and reducing the manual load. Experimental evaluation performed on two pre-defined protocols of the publicly available IdentifyMe dataset demonstrates the efficacy of the proposed model.
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