SynMorph: Generating Synthetic Face Morphing Dataset with Mated Samples

TL;DR

This paper introduces SynMorph, a high-quality synthetic face morphing dataset with 2450 identities and over 100,000 morphs, designed to improve face morphing attack detection and address data scarcity issues.

Contribution

We propose a novel method to generate a large-scale, high-quality synthetic face morphing dataset with diverse morphing algorithms and evaluate its effectiveness for training and benchmarking MAD algorithms.

Findings

Synthetic dataset outperforms existing datasets in quality and diversity.

Improves the training of morphing attack detection algorithms.

Demonstrates better generalization in face recognition vulnerability analysis.

Abstract

Face morphing attack detection (MAD) algorithms have become essential to overcome the vulnerability of face recognition systems. To solve the lack of large-scale and public-available datasets due to privacy concerns and restrictions, in this work we propose a new method to generate a synthetic face morphing dataset with 2450 identities and more than 100k morphs. The proposed synthetic face morphing dataset is unique for its high-quality samples, different types of morphing algorithms, and the generalization for both single and differential morphing attack detection algorithms. For experiments, we apply face image quality assessment and vulnerability analysis to evaluate the proposed synthetic face morphing dataset from the perspective of biometric sample quality and morphing attack potential on face recognition systems. The results are benchmarked with an existing SOTA synthetic dataset and a representative non-synthetic and indicate improvement compared with the SOTA. Additionally, we design different protocols and study the applicability of using the proposed synthetic dataset on training morphing attack detection algorithms.