MATLAB-Based Facial Image Attendance System

This project develops a facial recognition system on the MATLAB platform. Traditional facial recognition methods often rely on direct head comparisons, which offer limited practical applications and innovation. However, this project introduces a novel recognition approach that enhances both originality and utility. The system operates through a multi-stage pipeline: First, facial data from target individuals is collected and used to train models that extract distinctive facial features using eigenvalue computation algorithms. During the testing phase, the system processes complete upper-body or full-body photographs through several computational steps - face detection using Viola-Jones or deep learning algorithms, facial region segmentation through image cropping techniques, and dimensionality reduction via PCA (Principal Component Analysis) or LDA (Linear Discriminant Analysis) methods. The processed facial data is then compared against the database using similarity measurement techniques, outputting identified faces along with corresponding personal information. Additionally, the system incorporates attendance tracking functionality, providing administrators with automated management capabilities. The modular architecture allows for secondary development, enabling expansion to recognize faces outside the database. For unauthorized individuals, the system can integrate alarm mechanisms through callback functions or event triggers, significantly enhancing security protocols.