Attendance System Optimization through Deep Learning Face Recognition

Abstract

The significance of face recognition technology spans across diverse domains due to its practical applications. This study introduces an innovative face recognition system that seamlessly integrates Multi-task Cascaded Convolutional Neural Networks (MTCNN) for precise face detection, VGGFace for feature extraction, and Support Vector Machine (SVM) for efficient classification. The system demonstrates exceptional real-time performance in tracking multiple faces within a single frame, particularly excelling in attendance monitoring. Notably, the "VGGFace" model emerges as a standout performer, showcasing remarkable accuracy and achieving an impressive F-score of 95% when coupled with SVM. This underscores the model's effectiveness in recognizing facial identities, attributing its success to robust training on extensive datasets. The research underscores the potency of the VGGFace model, especially in collaboration with various classifiers, with SVM yielding notably high accuracy rates.