Learning A Shared Transform Model for Skull to Digital Face Image Matching
This work addresses the arduous task of human skull identification for law enforcement agencies, potentially speeding up the process and reducing manual load, but it appears incremental as it builds on existing datasets and protocols.
The paper tackled the problem of automating skull identification by matching skull images to digital face images, proposing a Shared Transform Model that learns discriminative representations and reduces intra-class variations, achieving efficacy demonstrated on the IdentifyMe dataset with two pre-defined protocols.
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.