The quantum data encoding techniques enable us to translate the classical data into the quantum states so that quantum algorithms and operations can be performed on the dataset. This paper identifies the key parameters which help to decide the most suitable data encoding technique for quantum-based face biometric systems. In this paper, four popular quantum data encoding approaches are considered: basic encoding, amplitude encoding, angle encoding and Hamiltonian encoding. Each method is evaluated based on its ability to handle high-dimensional facial data, circuit complexity, resource requirements, and potential quantum advantages. The paper also presents performance evaluations of these encoding techniques across specific face biometric tasks including face detection, verification, expression recognition, and facial data search. The study found that the amplitude encoding is most practical approach for near-term quantum face biometric systems. It concludes by identifying key challenges in quantum biometric systems and suggesting future research directions.
The quantum data encoding techniques enable us to translate the classical data into the quantum states so that quantum algorithms and operations can be performed on the dataset. This paper identifies the key parameters which help to decide the most suitable data encoding technique for quantum-based face biometric systems. In this paper, four popular quantum data encoding approaches are considered: basic encoding, amplitude encoding, angle encoding and Hamiltonian encoding. Each method is evaluated based on its ability to handle high-dimensional facial data, circuit complexity, resource requirements, and potential quantum advantages. The paper also presents performance evaluations of these encoding techniques across specific face biometric tasks including face detection, verification, expression recognition, and facial data search. The study found that the amplitude encoding is most practical approach for near-term quantum face biometric systems. It concludes by identifying key challenges in quantum biometric systems and suggesting future research directions.
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.
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.
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.
Quantum computing is based on the principles of quantum mechanics and uses quantum bits (qubits) that can exist in a state of superposition and entanglement. They promise a revolution in computing and information security. Firstly, using quantum algorithms in information technology opens up new possibilities for processing and protecting data. Secondly, biometric information security relies on a person's unique physical and behavioural characteristics, such as fingerprints, facial recognition, iris, voice, and others. These methods provide a high level of security due to their uniqueness and difficulty in counterfeiting. Combining the principles of quantum computing and biometric information security allows us to identify their similarities and integration capabilities to create reliable systems for protecting confidential information. While previous research in biometric information security has focused on individual aspects of decision-making to ensure the information security of educational institutions when using facial recognition technologies, this work presents a comprehensive framework combining several components into a single unified system for using various quantum cryptography algorithms. The main advantages of using quantum computing in object and image detection include accelerating the computational process using quantum components, robustness at different object angles, suitability for moving or static conditions, cryptographic noise immunity, and the ability to work in real practical conditions. The advantages of the quantum cryptographic scheme for face recognition technologies include ease of implementation, high cryptography security, the ability to parallelise the processes of encoding and decoding images, and the fact that complex computer equipment is not required. The study results provide a solid foundation for developing hybrid quantum computing and biometric information security technologies. Using the possibility of hybrid computing opens up new horizons for the standardisation and public dissemination of new technologies, and such a combination ensures reliable protection of confidential information and stimulates further research aimed at improving data protection methods in the context of the rapid development of the digital world.
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.
This study explores the integration of quantum data embedding techniques into classical machine learning (ML) algorithms; to assess performance enhancements and computational implications across a spectrum of models. We explored various classical-to-quantum mapping methods; ranging from basis encoding and angle encoding to amplitude encoding; for encoding classical data. We conducted an extensive empirical study encompassing popular ML algorithms, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines, and ensemble methods like Random Forest, LightGBM, AdaBoost, and CatBoost. Our findings reveal that quantum data embedding contributes to improved classification accuracy and F1 scores, particularly notable in models that inherently benefit from enhanced feature representation. We observed nuanced effects on running time, with low-complexity models exhibiting moderate increases and more computationally intensive models experiencing discernible changes. Notably, ensemble methods demonstrated a favorable balance between performance gains and computational overhead. This study underscores the potential of quantum data embedding to enhance classical ML models and emphasizes the importance of weighing performance improvements against computational costs. Future research may involve refining quantum encoding processes to optimize computational efficiency and explore scalability for real-world applications. Our work contributes to the growing body of knowledge on the intersection of quantum computing and classical machine learning, offering insights for researchers and practitioners seeking to harness the advantages of quantum-inspired techniques in practical scenarios.