ITDS | SPMS

A Facial Attendance App

Project Description
Supervisor

Manual student attendance in an institution is not a very efficient method because it may be susceptible to proxy attendance of absent students, and it is also time-consuming. This system develops a facial recognition attendance system based on modern deep learning models for autonomous recording of student attendance. The system uses a mobile frontend with a Flask backend and MySQL. Face detection is done using MTCNN for alignment, and InceptionResnetV1 trained on VGGFace2 for generating embeddings. This system marks attendance in real-time when faces are matched against the database with a cosine similarity level of 0.6. This system comprises student registration with duplicate face and ID identification, retrieving the attendance history, and student record management. This system is intended to increase the effectiveness and efficiency of attendance taking because the manual system is very slow. Following its implementation, the system produced positive outcomes in maintaining attendance records. This system contributes to digital transformation in education by automating attendance and ensuring integrity.

Machine Learning: Machine Learning (ML) research in Computer Science and Information Technology focuses on the development of algorithms and models that enable computers to learn from data and improve their performance over time without being explicitly programmed. It is a subset of Artificial Intelligence that uses statistical techniques to give machines the ability to learn patterns, make decisions, and predict outcomes based on data. Supervised learning, a key area of ML research, involves training models on labeled data, where the input-output relationships are predefined. This method is widely used for tasks such as classification (e.g., spam detection) and regression (e.g., predicting house prices). Unsupervised learning, on the other hand, involves finding hidden patterns in data without predefined labels, with clustering and association being typical applications in areas such as customer segmentation and anomaly detection. Reinforcement learning is another area of ML that focuses on teaching agents to make decisions by interacting with their environment and receiving feedback in the form of rewards or penalties. It is often applied in robotics, game playing, and autonomous systems, where continuous learning and adaptation are required.