Title: Managing employee attendance using real-time face recognition

Authors: Rajnesh Singh; Pushpa Singh; Richa Kumari Sharma; Priyanka Gupta; Narendra Singh; Sunil Gupta

Addresses: GL Bajaj Institute of Technology and Management, Plot No. 2, APJ Abdul Kalam Road, Knowledge Park III, Greater Noida, Uttar Pradesh, 201306, India ' GL Bajaj Institute of Technology and Management, Plot No. 2, APJ Abdul Kalam Road, Knowledge Park III, Greater Noida, Uttar Pradesh, 201306, India ' IEC-College of Engineering & Technology, Plot No. 4, Surajpur Kasna Road, Institutional Area, Knowledge Park I, Greater Noida, Uttar Pradesh, 201310, India ' IEC-College of Engineering & Technology, Plot No. 4, Surajpur Kasna Road, Institutional Area, Knowledge Park I, Greater Noida, Uttar Pradesh, 201310, India ' GL Bajaj Institute of Technology and Management, Plot No. 2, APJ Abdul Kalam Road, Knowledge Park III, Greater Noida, Uttar Pradesh, 201306, India ' University of Petroleum and Energy Studies, Bidholi Campus Office Energy Acres, P.O. Bidholi Via-Prem Nagar, Dehradun, Uttarakhand, 248007, India

Abstract: The process of the attendance monitoring system is changing due to different trends and technologies. If we review the past data on attendance tracking systems in industries, organisations and many companies use unique identification methodologies, such as RFID, fingerprint identification, eye recognition and facial recognition systems. Among all these strategies, face recognition is the most common and least time-consuming method. The system gives more flexibility and ease to employees and organisations in marking and managing their attendance. It employs the Haar Cascade Classifier for detecting the face and the local binary patterns histograms (LBPH) algorithm to train the classifier. After the successful entry of an employee's record in the system's dataset, his face will be detected by its name, and his attendance will be marked. After a particular time (depending on the organisation), for example, half an hour, the employee receives a confirmation email for the same. The proposed work provides efficient data management at the end of the Admin.

Keywords: face recognition; attendance; Haar cascade; LBPH; local binary patterns histograms; openCV; classifier; Python.

DOI: 10.1504/IJSSE.2023.134435

International Journal of System of Systems Engineering, 2023 Vol.13 No.4, pp.407 - 418

Received: 08 Aug 2022
Accepted: 03 Nov 2022
Published online: 23 Oct 2023 *

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