Open Access

ARTICLE

Real-Time Multimodal Biometric Authentication of Human Using Face Feature Analysis

Rohit Srivastava¹, Ravi Tomar¹, Ashutosh Sharma², Gaurav Dhiman³, Naveen Chilamkurti⁴, Byung-Gyu Kim^5,*

1 University of Petroleum and Energy Studies, Dehradun, India
2 Lovely Professional University, India
3 Government Bikram College of Commerce, Patiala, 147001, Punjab, India
4 La Trobe University, Melbourne, Australia
5 Sookmyung Women’s University, Seoul, 04310, Korea

* Corresponding Author: Byung-Gyu Kim. Email:

(This article belongs to the Special Issue: Management of Security, Privacy and Trust of Multimedia Data in Mobile devices communication)

Computers, Materials & Continua 2021, 69(1), 1-19. https://doi.org/10.32604/cmc.2021.015466

Received 23 November 2020; Accepted 08 January 2021; Issue published 04 June 2021

Abstract

As multimedia data sharing increases, data security in mobile devices and its mechanism can be seen as critical. Biometrics combines the physiological and behavioral qualities of an individual to validate their character in real-time. Humans incorporate physiological attributes like a fingerprint, face, iris, palm print, finger knuckle print, Deoxyribonucleic Acid (DNA), and behavioral qualities like walk, voice, mark, or keystroke. The main goal of this paper is to design a robust framework for automatic face recognition. Scale Invariant Feature Transform (SIFT) and Speeded-up Robust Features (SURF) are employed for face recognition. Also, we propose a modified Gabor Wavelet Transform for SIFT/SURF (GWT-SIFT/GWT-SURF) to increase the recognition accuracy of human faces. The proposed scheme is composed of three steps. First, the entropy of the image is removed using Discrete Wavelet Transform (DWT). Second, the computational complexity of the SIFT/SURF is reduced. Third, the accuracy is increased for authentication by the proposed GWT-SIFT/GWT-SURF algorithm. A comparative analysis of the proposed scheme is done on real-time Olivetti Research Laboratory (ORL) and Poznan University of Technology (PUT) databases. When compared to the traditional SIFT/SURF methods, we verify that the GWT-SIFT achieves the better accuracy of 99.32% and the better approach is the GWT-SURF as the run time of the GWT-SURF for 100 images is 3.4 seconds when compared to the GWT-SIFT which has a run time of 4.9 seconds for 100 images.

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Citations