International Journal of Science, Engineering and TechnologyAuthors: Digambar Santosh Awale, Sanika Arvind Golatkar, Dr. (Mrs.) Jasbir Kaur, Mr. Suraj Kanal, Ms. Mansi Rajapurkar
Abstract: Traditional attendance management techniques, including manual attendance registers and fingerprint-based biometric systems, often suffer from several limitations such as time consumption, hygiene concerns, and the possibility of proxy attendance. Although contactless facial recognition systems provide a more convenient alternative, they are still vulnerable to spoofing attacks using printed images or digital displays. To address these challenges, this paper introduces *Face-Track*, a multi-stage deep learning–based framework that combines real-time facial recognition with blink-based live-ness detection to develop a secure and spoof-resistant at-tendance monitoring system. The proposed framework em-ploys a Multi-task Cascaded Convolutional Neural Network (MTCNN) for accurate face detection and facial landmark extraction. For feature representation, a ResNet50 model pre-trained on the VGGFace2 dataset is utilized to generate 2048-dimensional facial embeddings. These embeddings are then classified using a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel for reliable identity recognition. To prevent spoofing attempts, the system integrates a lightweight Eye Aspect Ratio (EAR)–based liveness detection approach. This method monitors eye landmark movements to identify natural blinking patterns, thereby confirming the pres-ence of a real user. The entire system is implemented through a Streamlit-based web interface, while attendance records are automatically maintained using an SQLite database.
