Synthetic Ethnicity Alteration for Diversifying Face Datasets - Investigating Recognition and Quality

The rapid advancement of biometric authentication requires extensive performance tests to inhibit the discriminatory treatment of travellers due to their demographic background. However, the European General Data Protection Regulation (GDPR) restricts the use of face images collected as part of border controls to be processed for no reason other than their original purpose. In this work, we explore altering the ethnicity using synthetic face image generation approaches to increase the diversity of datasets to represent multiple ethnicities of the same identity. In this work, we present a systematic study to assess the applicability of ethnicity alteration of face images by studying the performance of the face recognition system (FRS) and face image quality assessment (FIQA). Ethnicity-altered images were generated using Generative Adversarial Network-based (GAN) image-to-image translation and manifold learning models. We test these approaches on a publicly available Racial Faces in the Wild (RFW) dataset and focus on Asian, Black, and Indian ethnicity. We conduct experiments to benchmark the ethnicity-altered images for FRS performance using two recent deep learning models and complement it with FIQA analysis using three different algorithms. With the limited drop in FRS performance and a similar effect on quality scores, our results indicate the applicability of ethnicity alteration to diversify the datasets and make systems fair.