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@article{148148,
        author = {Anju V Das and Anjana Shyju and Thomas Varghese and Nisha Mohan P M},
        title = {Face Recognition Based Attendance Management System Using Machine Learning },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {5},
        number = {12},
        pages = {740-743},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=148148},
        abstract = {Attendance is a compulsory requirement of every organization. Maintaining attendance register daily is a difficult and time consuming task. There are many automated methods for the same available like Biometric, RFID, eye detection, voice recognition, and many more. This paper provides an efficient and smart method for marking attendance. As it is known that primary identification for any human is its face, face recognition provides an accurate system which overcomes the ambiguities like fake attendance, high cost, and time consumption. This system uses face recognizer library for facial recognition and storing attendance. It has a camera that captures input image, an algorithm to detect a face from the input image, encode it and recognize the face and mark the attendance in a spreadsheet. The system camera of an android phone captures the image and sends it to the server where faces are recognized from the database and attendance is calculated on basis of it. The purpose of reducing the errors that occur in the traditional attendance taking system has been achieved by implementing this automated attendance system using deep learning. Face recognition system have been presented using deep learning which exhibits robustness towards recognition of the users with accuracy of 98.3% and result is converted into a PDF.},
        keywords = {Android application, Biometric, Recognition system, Face Recognition, Deep Learning, Portable Document Format.},
        month = {},
        }