Automated Attendance Marking System Using CNN-Based Face Recognition
M. Mahesh Kumar, Sonti Ramya sri, T. Meghana · Industrial Engineering Journal · 2025
The project is a Flask-based application designed for face recognition and attendance tracking. It includes functionality for training a convolutional neural network (CNN) model using the InceptionV3 architecture, which is fine-tuned to classify faces. The application supports registering users by capturing and saving their facial images, which are then organized into directories. These images undergo data augmentation to enhance model robustness during training. A pre-trained CNN model is employed to detect and identify faces, allowing attendance to be marked automatically based on face recognition results. Attendance data is recorded in an Excel file, where new entries are added dynamically for each date.The application also provides an analysis feature that processes attendance data, generates visual summaries for different dates, and displays trends in a userfriendly format. Interactive web pages enable users to train the model, register faces, upload images for marking attendance, and review attendance statistics. The project incorporates image preprocessing, realtime face detection, and the use of cascaded classifiers, with seamless integration of attendance tracking into the system. The intuitive interface and automated processes aim to enhance efficiency and accuracy in attendance management.