MATLAB Toolbox Functions for Video-based Face Detection and Recognition

This documentation introduces a MATLAB-implemented toolbox function suite for video-based face detection and recognition. These functions provide comprehensive solutions for detecting and identifying human faces in video sequences. Built upon MATLAB's robust computational framework and advanced algorithms, the toolbox employs computer vision techniques including Haar cascade classifiers, convolutional neural networks (CNN), or deep learning approaches for accurate face localization and feature extraction. Key functions include video frame preprocessing, face region detection using Viola-Jones algorithm variants, feature vector extraction through Principal Component Analysis (PCA) or Local Binary Patterns (LBP), and recognition via support vector machines (SVM) or Euclidean distance matching. Users can efficiently process video streams, extract critical facial data, and implement real-time recognition systems. The toolbox simplifies complex computer vision tasks through modular function design, including videoReader() for frame extraction, detectFaces() implementing cascade object detection, and recognizeFaces() using feature matching algorithms. This makes it particularly valuable for security surveillance applications, biometric authentication systems, and video content analysis, significantly enhancing efficiency in face detection and recognition workflows while maintaining high accuracy rates.