SmartAttend: A CNN-Based Smart Attendance System with Face Recognition and Liveness Detection

Dr. Indhumathi S K, Meghana B, Likhitha V

Attendance systems built around manual roll calls or RFID and biometric cards still fall prey to proxy attendance, take up too much of an instructor's time, and leave a paper trail that is hard to audit after the fact — a problem that only gets worse as class sizes grow and the time faculty can spend verifying each student shrinks. SmartAttend was built to close these gaps. It is a Streamlit-based attendance platform that pairs CNN-driven face recognition with CNN-driven liveness detection and a sessionaware decision engine, so that a printed photo or a phone held up to the camera cannot be marked present in place of the real student. Faces are located using a Multi-task Cascaded Convolutional Network (MTCNN), with an OpenCV Haar-cascade detector kept on hand as a fallback; identity is then confirmed by a trained CNN classifier, backed up by a cosine-similarity matcher when no trained model is available; and a dedicated liveness CNN screens out printed-photo and screen-replay spoofing attempts. Attendance is only recorded when four things line up at once — a claimed roll number, a recognized face, a passed liveness check, and an open class session — and every attempt, whether it succeeds or not, is written to a log that can be reviewed later, with mismatches routed into a separate exception queue. This paper walks through the system's architecture, how the face and liveness models were trained, the reasoning behind the dual-logging and role-based-access design, and a local evaluation in which the liveness model hit 1.0000 across accuracy, precision, recall, F1-score, and ROC-AUC on a 35-sample test set once its threshold was recalibrated from 0.30 to 0.4947. The paper closes by discussing what a small evaluation set can and can't tell us, how the system is deployed on managed Postgres and S3-backed infrastructure, and where the project could go if it were to grow into a full multi-classroom platform.