Smart Classroom Attendance System Using Facial Biometrics
DOI:
https://doi.org/10.32628/IJSRSET2512545Keywords:
Face Recognition System, TensorFlow, TFlearn, OpenCV, Tkinter, Image ProcessingAbstract
Facial recognition is a reliable and widely used method for identifying individuals based on their unique facial features. This technology plays a crucial role in applications such as security, access control, and personal authentication. A face recognition system typically operates in two key stages: face detection and face recognition. The detection phase identifies whether an image contains a face, while the recognition phase matches the detected face to a stored identity or database. This paper focuses on designing and implementing a robust face recognition system using advanced image processing techniques. It leverages popular libraries such as TensorFlow and TFlearn for building the recognition model. These tools provide a flexible framework for developing machine learning algorithms, enabling accurate detection and identification of faces from input images. Additionally, the paper introduces a feature for generating audio feedback by recording and storing the names of authorized individuals. This enhances the system's usability by providing audible confirmation of recognized individuals, making it suitable for various interactive applications. The integration of OpenCV for image handling and Tkinter for creating a user-friendly graphical interface further enriches the system's functionality. By combining advanced image processing methods and intuitive design, this work showcases a practical approach to real-time face recognition, offering a versatile solution for real-world scenarios.
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