Synthetic Forehead-creases Biometric Generation for Reliable User Verification

TL;DR

This paper introduces a novel framework for generating realistic synthetic forehead-crease images to enhance biometric verification, addressing data scarcity and privacy concerns in face recognition, especially when masks cover facial features.

Contribution

The paper presents a new diffusion-model-based framework for synthesizing identity-aware forehead-crease images, improving biometric verification accuracy with synthetic data.

Findings

Synthetic images have high realism and diversity.

Synthetic data improves verification accuracy.

The framework effectively models individual forehead creases.

Abstract

Recent studies have emphasized the potential of forehead-crease patterns as an alternative for face, iris, and periocular recognition, presenting contactless and convenient solutions, particularly in situations where faces are covered by surgical masks. However, collecting forehead data presents challenges, including cost and time constraints, as developing and optimizing forehead verification methods requires a substantial number of high-quality images. To tackle these challenges, the generation of synthetic biometric data has gained traction due to its ability to protect privacy while enabling effective training of deep learning-based biometric verification methods. In this paper, we present a new framework to synthesize forehead-crease image data while maintaining important features, such as uniqueness and realism. The proposed framework consists of two main modules: a Subject-Specific Generation Module (SSGM), based on an image-to-image Brownian Bridge Diffusion Model (BBDM), which learns a one-to-many mapping between image pairs to generate identity-aware synthetic forehead creases corresponding to real subjects, and a Subject-Agnostic Generation Module (SAGM), which samples new synthetic identities with assistance from the SSGM. We evaluate the diversity and realism of the generated forehead-crease images primarily using the Fr\'echet Inception Distance (FID) and the Structural Similarity Index Measure (SSIM). In addition, we assess the utility of synthetically generated forehead-crease images using a forehead-crease verification system (FHCVS). The results indicate an improvement in the verification accuracy of the FHCVS by utilizing synthetic data.