Real-Time Face Recognition and Detection System Implementation in MATLAB

In this document, I present the development of a real-time face recognition and detection system implemented in MATLAB. The system employs computer vision algorithms such as Viola-Jones object detection framework for face localization and feature-based recognition methods like Local Binary Patterns (LBP) or PCA-based eigenfaces for identification. Key MATLAB functions including vision.CascadeObjectDetector for real-time face detection and custom feature extraction functions are utilized to process video streams frame-by-frame. The implementation involves continuous video capture using MATLAB's Image Acquisition Toolbox, where each frame undergoes preprocessing (resizing and grayscale conversion) before detection. The recognition module compares extracted facial features against a pre-trained database using similarity matching algorithms, achieving real-time performance through optimized matrix operations native to MATLAB. This system enhances security applications like surveillance and biometric authentication by providing instantaneous results via MATLAB's graphical interface components. The code structure allows customization through adjustable detection thresholds, training dataset modifications, and integration with hardware accelerators. MATLAB's built-in optimization ensures reliable performance with minimal latency, making it suitable for deployment in access control systems and real-time monitoring scenarios. The implementation demonstrates MATLAB's capability in handling image processing tasks through efficient code vectorization and parallel computing tools, ensuring robust operation under varying lighting conditions and orientations. Future enhancements could incorporate deep learning approaches using MATLAB's Neural Network Toolbox for improved accuracy.