Differential Morphed Face Detection Using Deep Siamese Networks
This research is significant for border control and security applications, as it aims to prevent two or more individuals from using the same passport due to successful morphing attacks.
The paper addresses the vulnerability of facial recognition systems to morphing attacks, where a single facial image can verify multiple identities. It proposes a novel differential morph attack detection framework using a deep Siamese network, comparing its performance against classical and deep learning models on VISAPP17 and MorGAN datasets.
Although biometric facial recognition systems are fast becoming part of security applications, these systems are still vulnerable to morphing attacks, in which a facial reference image can be verified as two or more separate identities. In border control scenarios, a successful morphing attack allows two or more people to use the same passport to cross borders. In this paper, we propose a novel differential morph attack detection framework using a deep Siamese network. To the best of our knowledge, this is the first research work that makes use of a Siamese network architecture for morph attack detection. We compare our model with other classical and deep learning models using two distinct morph datasets, VISAPP17 and MorGAN. We explore the embedding space generated by the contrastive loss using three decision making frameworks using Euclidean distance, feature difference and a support vector machine classifier, and feature concatenation and a support vector machine classifier.