Hand-Based Person Identification Using Global and Part-Aware Deep Feature Representation Learning

The aim of this research is to identify biometric traits of dorsal hand images, which are the most commonly documented aspect of perpetrator in child sexual abuse imagery. In this work, the researchers propose hand-based person identification by learning both global and local deep feature representations. Using Global and Part-Aware Network (GPA-Net), the researchers created global and local branches on the conv-layer for learning robust discriminative global and part-level features. Similar research has been conducted at Auckland University.
Artificial intelligenceprosecutionChild Sexual Abuse Material (CSAM)Machine learning
Research (peer reviewed)