Face recognition in the presence of age and quality variations poses a formidable challenge. While recent margin-based loss functions have shown promise in addressing these variations individually, real-world scenarios such as selfie versus ID face matching often involve simultaneous variations of both age and quality. In response, we propose a comprehensive framework aimed at mitigating the impact of these variations while preserving vital identity-related information crucial for accurate face recognition. The proposed adaptive margin-based loss function AQUAFace exhibits adaptiveness towards hard samples characterized by significant age and quality variations. This loss function is meticulously designed to prioritize the preservation of identity-related features while simultaneously mitigating the adverse effects of age and quality variations on face recognition accuracy. To validate the effectiveness of our approach, we focus on the specific task of selfie versus ID document matching. Our results demonstrate that AQUAFace effectively handles age and quality differences, leading to enhanced recognition performance. Additionally, we explore the benefits of fine-tuning the recognition model with synthetic data, further boosting performance. As a result, our proposed model, AQUAFace, achieves state-of-the-art performance on six benchmark datasets (CALFW, CPLFW, CFP-FP, AgeDB, IJB-C, and TinyFace), each exhibiting diverse age and quality variations.

This paper delves into the challenging task of selfie vs ID face verification which involves matching high-resolution selfies with low-resolution faces extracted from scanned ID documents. Existing face verification models often face performance degradation when confronted with this task, mainly due to disparities in data distributions, such as age-difference, degradation due to scanning, and difference in appearance. To address this issue and enhance performance, the paper explores the implementation of facial quality assessment and hard-pair mining techniques. In addition, the paper investigates the potential of synthetic data for training face verification models tailored for this specific task. The integration of synthetic data as an alternative training source is explored to improve robustness and overcome legal and privacy concerns arising from authentic datasets. By combining hard pair mining, facial quality assessment, and the utilization of synthetic data, this paper presents a comprehensive framework that aims to achieve improved face verification results in the complex scenario of selfie vs ID matching. The goal is to optimize the models’ performance and enhance their ability to accurately match selfies with the corresponding ID images, even under challenging conditions.