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BASRA JOURNAL OF SCIENCE

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

Farah Saad Al-Mukhtar, Rana M. Hasan, Raghad AbdulHadi AbdulQader

Published Aug 31, 2026• 0 citations
Publisher

Abstract

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.

DOI
10.29072/basjs.20260211

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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.
Hybrid quantum methods have received limited study for resource-constrained facial biometrics. We present a hybrid quantum-classical facial recognition pipeline designed to run on standard computing hardware. Images undergo gamma correction, contrast enhancement, and principal component analysis before their features are encoded into an eight-qubit variational quantum classifier. Classical image matching then performs recognition. In experiments with 50,000 images, comprising 25,000 faces from CelebA and 25,000 non-face images from CIFAR-10, the method outperformed the reported CPU-trained FaceNet baseline in accuracy and training efficiency. The pipeline was also evaluated on GPU and quantum hardware. In an attendance monitoring deployment at Mahindra University in collaboration with Lloyds Technology Centre, CPU inference took 0.2 to 0.5 seconds per person, and the system remained robust to the use of spectacles. These findings support the feasibility of deploying hybrid quantum methods for facial recognition on existing CPU hardware.
Face recognition has become an essential biometric technology for applications such as security monitoring, user authentication, and intelligent surveillance systems. Conventional approaches based on deep neural networks, particularly convolution-based architectures, provide high recognition performance but often demand considerable computational resources and extended training durations as network complexity increases. To overcome these challenges, Quantum Machine Learning (QML) utilizes quantum computing concepts, including quantum superposition and entangled states, to achieve efficient feature representation and processing. This paper presents a Hybrid Quantum-Classical Face Recognition System built on a Multi-Gate Quantum Convolutional Neural Network (MG-QCNN). The proposed framework employs a 4-qubit variational quantum circuit that incorporates RY state encoding together with RX, RZ, and CNOT quantum operations to extract discriminative facial representations. In addition, a structural ablation analysis is conducted to investigate the influence of individual quantum components on the overall recognition capability of the model. To enhance spatial feature learning and classification performance, several classical neural network layers, namely convolution, batch normalization, max-pooling, dropout, and fully connected layers, are integrated after the quantum feature extraction stage. Within the hybrid framework, the quantum circuit is responsible for generating meaningful feature representations, whereas the classical neural network performs feature refinement and final identity classification. The framework is further combined with the DeepFace verification approach to perform facial similarity analysis and identity verification. Moreover, an Open-Set Face Recognition mechanism is incorporated to determine if an input facial image corresponds to a registered identity or represents an unseen individual. This capability increases system reliability and supports accurate unknown-person identification
Face recognition systems are widely used for biometric authentication but face two major problems. First, processing high-resolution images and large databases requires extensive computational time. Second, emerging quantum computers threaten to break the encryption methods that protect stored facial templates. Quantum computers will soon be able to decrypt current security systems, putting biometric data at permanent risk since facial features cannot be changed like passwords. This paper presents a solution that uses quantum computing to speed up face recognition while adding quantum-resistant security. It applies quantum principal component analysis (QPCA) and the SWAP test to reduce the computational complexity and implement lattice-based cryptography, which quantum computers cannot break. Experimental evaluation demonstrates a significant overall speedup with improved accuracy. The proposed framework achieves a significant improvement in performance, provides 125-bit security against quantum attacks and compresses the data storage requirements significantly. These results demonstrate that quantum-enhanced face recognition can solve both the speed and security challenges facing current biometric systems, making it practical for real-world deployment as quantum technology advances.
The edge computing environment faces a challenge in maintaining data privacy while performing machine learning tasks. The current solutions for privacy preservation are inefficient or do not provide security guarantees, causing a critical gap in the edge computing environment, which is resource-constrained. The aim of this research work is to design a hybrid quantum-classical computing system that addresses the challenges of privacy preservation and efficiency in machine learning tasks. We design a novel architecture combining variational quantum circuits for secure data encoding with classical neural networks for scalable inference, implemented through federated learning protocols enhanced with quantum differential privacy mechanisms. Experimental evaluation demonstrates a 35% reduction in communication overhead compared to traditional federated learning approaches while maintaining 99.2% classification accuracy across diverse datasets. The quantum-enhanced privacy mechanisms achieve provable security guarantees with information-theoretic bounds, outperforming conventional cryptographic methods by 28% in computational overhead reduction. This hybrid approach establishes a new paradigm for secure and efficient machine learning deployment on edge devices, enabling privacy-preserving artificial intelligence in healthcare, finance, and IoT applications.
Facial biometrics play a crucial role in identity verification, yet classical approaches face challenges related to computational complexity and security vulnerabilities. This paper explores the integration of quantum computing with eigenface-based face verification to enhance efficiency and security. By utilizing the advantages of Quantum Principal Component Analysis (QPCA), we achieve exponential speedups in eigenvalue decomposition, significantly reducing the computational burden of high-dimensional facial data processing. Our hybrid classical-quantum approach optimizes quantum state encoding and similarity measurement via the Swap Test techniques. Experimental results demonstrate improved verification accuracy and scalability compared to classical eigenfaces, particularly for large databases. Despite current hardware constraints, our findings establish a foundational framework for quantum-enhanced biometric systems. This work highlights the potential of quantum computing in facial recognition, and prepares the way for more efficient, secure, and scalable biometric authentication systems.
This paper presents a hybrid classical-quantum algorithm for face biometric verification that enhances the traditional eigenface method using Quantum Principal Component Analysis (QPCA). Classical preprocessing is used for image normalization and mean subtraction, while QPCA performs efficient eigen decomposition of facial data. Quantum projection and similarity measurement are implemented using SWAP test circuits, enabling exponential speedup in highdimensional operations. The proposed system significantly reduces computational complexity, particularly in eigenvalue decomposition, without compromising recognition accuracy. Our implementation demonstrates measurably improved verification performance compared to classical methods, while substantially reducing processing time. Simulation results on reduced-size facial datasets validate the feasibility and computational advantages of our approach. Comparative analysis demonstrates scalability advantages for high-resolution images and large datasets. This work lays a foundation for efficient and secure biometric authentication systems by utilizing the quantum computing advancements.