  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.
