MATLAB Code Implementation for Face Recognition

This face recognition source code system consists of multiple interconnected modules. The primary component is face detection, which implements algorithms like Viola-Jones or deep learning-based methods to locate and identify facial regions within images using techniques such as Haar cascades or CNN-based bounding box regression. The second critical module handles face recognition, employing advanced algorithms like Eigenfaces, Fisherfaces, or deep neural networks (e.g., Siamese networks) to extract facial features and compare them against known datasets for identity verification and authentication. Additional functionalities may include facial expression recognition using classification models (SVM or CNN) and gender identification through feature-based or deep learning approaches. The system architecture provides a reliable and efficient face recognition solution suitable for various applications, with modular code structure allowing customization of detection parameters, feature extraction methods, and matching thresholds.