Quantum-Enhanced Eigenface Algorithm for Face Verification

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.