Next-Generation Biometric Authentication: Overcoming the Twin Identification Challenge with Advanced Facial Recognition and Multi-Modal Analysis Techniques - Hasan Nsaif , Azhar; Abduladheem Hasan, Rawsam (Journal of Al-Qadisiyah for Computer Science and Mathematics, 2025)
Conventional facial recognition struggles with individuals sharing near-identical facial features, particularly monozygotic twins. This research introduces a robust, real-time methodology to overcome this by integrating geometric, textural, and dynamic facial characteristics. The framework employs Multi-Task Cascaded Convolutional Networks (MTCNN) for face detection and alignment, followed by FaceNet for 128-dimensional facial embedding generation. MediaPipe's 468-point facial landmark extraction quantifies subtle structural variations via transformation matrix analysis and blend-shape evaluation, capturing static geometric discrepancies and dynamic micro-expressions. Validated on 7,200-image dataset (70% training, 30% testing), the system achieved 97.73% accuracy, operating efficiently on consumer-grade GPUs. This approach significantly enhances biometric technology, offering improved identity verification for genetically similar individuals in critical security applications like border control and secure access management, thereby addressing a key limitation in current facial recognition systems.