A Hybrid Classical-Quantum Processing Channel intended for Privacy-Preserving Facial Recognition

Identity verification is a prevalent application of facial recognition systems, but the traditional feature embeddings used in the systems create biometric data vulnerable to many privacy violations. Traditional privacy-preserving solutions are typically either recognition-accuracy compromised or have high computational costs, making them ineffective in real, resource-bounded world settings. Although the current classical methods cannot offer intrinsic template protection without affecting the performance, the current hybrid quantum-classical methods do not have an end-to-end deployment-friendly architecture that has been demonstrated to be reliable in facial recognition in the presence of realistic adversarial and inversion attacks. To solve these problems, a hybrid classical-quantum processing pipeline is suggested. Here, lightweight convolutional feature extraction is done fully on-device, followed by the encoding of the normalized embeddings into quantum states and conversion by a variational quantum circuit (VQC). Privacy is physically enforced, so that only non-invertible measurements of quantum measurements are sent to infer classically. Experimental analyses prove the suggested model to be significantly better than lightweight and privacy-oriented baselines. In particular, LFW and CelebA accuracies reach 94.3% and 92.8%, respectively, with zero template recoverability and inference latency of less than 200 ms, which proves the strength and efficiency of the system in the edge applications, as well as the guidelines for prospective authors who will have to prepare the final manuscript accepted for publication.