Student Attendance Tracking Management using Face Biometric Smart System

The “Face Recognition-Based Smart Attendance Management System” proposes an innovative solution for automating attendance tracking in educational and corporate settings. This system employs a series of pre-processing techniques including face alignment and illumination normalization to enhance the quality of input images. Feature extraction is achieved through the utilization of deep learning methods such as Transductive Support Vector Machine (TSVM) and Histogram of Oriented Gradients (HOG) for capturing facial landmarks and unique features. The recognition stage utilizes state-of-the-art algorithms like FaceNet and VGGFace to accurately identify individuals, ensuring efficient and accurate attendance management with real-time data updates. In addition to its robust feature extraction and recognition techniques, the system incorporates data storage and management components, making it capable of maintaining a comprehensive database of student or employee attendance records. Through continuous learning and adaptation, the system ensures consistent performance across varying lighting conditions and poses. Furthermore, it can provide real-time feedback to instructors or administrators, facilitating swift responses to attendance irregularities. This smart attendance management system not only simplifies record-keeping but also enhances security and accountability, offering a reliable and efficient solution for attendance monitoring in diverse educational and professional environments.