Leveraging Synthetic Data and Hard Pair Mining for Selfie vs ID Face Verification

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.