Cross-Domain Identity Representation for Skull to Face Matching with Benchmark DataSet

TL;DR

This paper introduces a deep learning framework using Siamese networks to match skull X-ray images to facial images for forensic identification, validated on a custom dataset of 40 volunteers.

Contribution

It presents a novel cross-domain identity recognition method using Siamese networks trained on a new dataset of skull and face images.

Findings

Satisfactory identification accuracy achieved

Effective cross-domain feature learning demonstrated

New dataset of skull-face pairs created for research

Abstract

Craniofacial reconstruction in forensic science is crucial for the identification of the victims of crimes and disasters. The objective is to map a given skull to its corresponding face in a corpus of faces with known identities using recent advancements in computer vision, such as deep learning. In this paper, we presented a framework for the identification of a person given the X-ray image of a skull using convolutional Siamese networks for cross-domain identity representation. Siamese networks are twin networks that share the same architecture and can be trained to discover a feature space where nearby observations that are similar are grouped and dissimilar observations are moved apart. To do this, the network is exposed to two sets of comparable and different data. The Euclidean distance is then minimized between similar pairs and maximized between dissimilar ones. Since getting pairs of skull and face images are difficult, we prepared our own dataset of 40 volunteers whose front and side skull X-ray images and optical face images were collected. Experiments were conducted on the collected cross-domain dataset to train and validate the Siamese networks. The experimental results provide satisfactory results on the identification of a person from the given skull.