Title: Edge feature enhanced convolutional neural networks for face recognition using IoT devices

Authors: Ankur; Mohit Kumar Rohilla; Rahul Gupta

Addresses: Wipro Technologies, Knowledge Park 4, Gautam Budha Nagar, Greater Noida, U.P., 201308, India ' Tata Consultancy Services Skyview Corporate Park, Sector-74A, Gurgoan, Haryana, 122004, India ' Kurukshetra University, Kurukshetra, Haryna, 136119, India

Abstract: COVID-19 pandemic has turned the world upside down, with almost everything coming to a halt. In the current period, where we are slowly returning to normal lives, organisations have become more concerned about safety and health. In the post-COVID period, biometric systems based on fingerprint can be dangerous; moreover, real-time attendance of employees and students joining from online mode is a challenge. Real-time face recognition is a challenging task in terms of accuracy and reliability, especially when deep convolutional neural networks (DCNN) are used for face recognition. DCNNs are data-hungry, and in real-life scenarios, the amount of data per subject or class is minimal, and the number of subjects/classes can be huge. Hence, the need for research on image processing and data augmentation research arises for face recognition as there are many scenarios where the number of classes (subjects) is vast.

Keywords: face recognition; edge enhancement; face edge processing; deep convolutional neural network; DCNN; data augmentation; image processing.

DOI: 10.1504/IJCVR.2024.136994

International Journal of Computational Vision and Robotics, 2024 Vol.14 No.2, pp.119 - 153

Received: 08 Apr 2021
Accepted: 14 Jun 2022
Published online: 01 Mar 2024 *

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