MATLAB-Based Face Recognition Image Attendance System

This project develops a face recognition system based on the MATLAB platform. Traditional face recognition methods typically involve direct head-to-head comparison, which offers limited practical value and innovation. Our system's recognition principle follows a structured approach: first, collect facial images of target individuals for training to extract facial feature vectors. During testing, the system processes complete upper-body or full-body photographs by performing face detection and localization, segmenting the facial region, applying dimensionality reduction techniques, and comparing against the image database. The output includes the identified face along with relevant personal information, while automatically generating attendance statistics. The system implementation likely utilizes MATLAB's Image Processing Toolbox for image preprocessing, possibly employing Viola-Jones algorithm for face detection through vision.CascadeObjectDetector. Feature extraction may involve PCA (Principal Component Analysis) for dimensionality reduction or LBP (Local Binary Patterns) for texture analysis. Database matching could be implemented using similarity measurement functions like pdist2 for Euclidean distance calculation between feature vectors. For secondary development, the system can be extended to differentiate between authorized and unauthorized individuals. Unrecognized faces outside the database could trigger alarm mechanisms using MATLAB's audio/video alert functions. Furthermore, this face recognition technology finds applications across various domains including security surveillance and personnel attendance management. The advancement of face recognition technology continues to expand possibilities and convenience in modern security systems. The code structure would typically include modules for: image acquisition (imread), preprocessing (imresize, rgb2gray), face detection (vision.CascadeObjectDetector), feature extraction (pca, lbp), database management (dataset arrays), and matching algorithms (knnsearch). Attendance logging would involve file I/O operations (fprintf, xlswrite) for record keeping.