  Numerous studies have shown that existing Face Recognition Systems (FRS),
including commercial ones, often exhibit biases toward certain ethnicities due
to under-represented data. In this work, we explore ethnicity alteration and
skin tone modification using synthetic face image generation methods to
increase the diversity of datasets. We conduct a detailed analysis by first
constructing a balanced face image dataset representing three ethnicities:
Asian, Black, and Indian. We then make use of existing Generative Adversarial
Network-based (GAN) image-to-image translation and manifold learning models to
alter the ethnicity from one to another. A systematic analysis is further
conducted to assess the suitability of such datasets for FRS by studying the
realistic skin-tone representation using Individual Typology Angle (ITA).
Further, we also analyze the quality characteristics using existing Face image
quality assessment (FIQA) approaches. We then provide a holistic FRS
performance analysis using four different systems. Our findings pave the way
for future research works in (i) developing both specific ethnicity and general
(any to any) ethnicity alteration models, (ii) expanding such approaches to
create databases with diverse skin tones, (iii) creating datasets representing
various ethnicities which further can help in mitigating bias while addressing
privacy concerns.
