AI
DIF-V: A Diverse, Inclusive Synthetic Face Dataset for Fairer Verification
Face checks power everything from phone unlocks to banking logins — but training data often skews toward certain races and genders. This paper presents a method to generate high-quality, ID‑photo‑style synthetic faces that better reflect real‑world diversity, and introduces a new benchmark: Diverse and Inclusive Faces for Verification