On the Illusion of Gender Bias in Face Recognition: Explaining the Fairness Issue Through Non-demographic Attributes

ArXi:2501.12020v2 Announce Type: replace Face recognition systems (FRS) exhibit significant accuracy differences based on the user's gender. Since such a gender gap reduces the trustworthiness of FRS, recent efforts have tried to find the causes. However, these studies make use of manually selected, correlated, and small-sized sets of facial features to their claims. In this work, we analyze gender bias in face recognition by successfully extending the search domain to decorrelated combinations of 40 non-graphic facial characteristics. First, we