Preserving Gender Privacy in Remote Face Verification with Human-Machine EvaluationRaza, Ashir (Avtor) Peer, Peter (Mentor) Rot, Peter (Komentor) face recognitionsoft biometricsprivacycomputer visionhuman perceptionFaces reveal more than identity: they also carry soft biometric attributes such as gender. Soft biometric privacy-enhancing techniques (SB-PETs) modify biometric data to make such attributes harder to infer while preserving their utility for identity verification. Existing fusion-based SB-PETs, which fuse facial regions from an opposite-gender face, are evaluated mostly against automated classifiers, leaving open whether the enhancement also holds when a human views the same image. We focus on gender and propose PriDSS++, an extension of PriDSS that pairs each face with a ranked set of opposite-gender candidates, transfers the selected partner’s gender-contextual regions, and keeps identity-critical regions intact. A rank-based fallback selects the next candidate when needed to enforce both privacy and identity preservation. Unlike prior fusion-based work, PriDSS++ is positioned in a remote client-server setting in which a human operator may later inspect the stored enhanced images, a scenario that existing evaluations do not address. PriDSS++ reduces gender classification performance from an area under the ROC curve (AUC) of 0.990 to 0.561 while preserving identity verification. Two perception studies with 93 and 50 participants, respectively, rate its images as near-photorealistic and identity-preserving. However, participants could still identify gender, even in images for which the classifier was maximally uncertain. Deceiving a classifier therefore does not imply deceiving a human observer, so soft-biometric privacy enhancement should be evaluated against both.20262026-11-03 09:55:02Magistrsko delo190755sl