Open Access

ARTICLE

Secure Rotation Invariant Face Detection System for Authentication

Amit Verma¹, Mohammed Baljon², Shailendra Mishra^2,*, Iqbaldeep Kaur¹, Ritika Saini¹, Sharad Saxena³, Sanjay Kumar Sharma⁴

1 Department of Computer Science & Engineering, Chandigarh Group of Colleges, Mohali, 140317, India
2 Department of Computer Engineering, College of Computer & Information Science, Majmaah University, 11952, Saudi Arabia
3 Department of Computer Science and Engineering, Thapar Institute of Engineering & Technology, Patiala, 147004, India
4 Department of Computer Centre & Campus Network Facility Science Complex, PO Central University, Gachibowli, Hyderabad, 500046, Telangana, India

* Corresponding Author: Shailendra Mishra. Email:

Computers, Materials & Continua 2022, 70(1), 1955-1974. https://doi.org/10.32604/cmc.2022.020084

Received 07 May 2021; Accepted 08 June 2021; Issue published 07 September 2021

Abstract

Biometric applications widely use the face as a component for recognition and automatic detection. Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and rotation. This problem has been investigated, and a novice algorithm, namely RIFDS (Rotation Invariant Face Detection System), has been devised. The objective of the paper is to implement a robust method for face detection taken at various angle. Further to achieve better results than known algorithms for face detection. In RIFDS Polar Harmonic Transforms (PHT) technique is combined with Multi-Block Local Binary Pattern (MBLBP) in a hybrid manner. The MBLBP is used to extract texture patterns from the digital image, and the PHT is used to manage invariant rotation characteristics. In this manner, RIFDS can detect human faces at different rotations and with different facial expressions. The RIFDS performance is validated on different face databases like LFW, ORL, CMU, MIT-CBCL, JAFFF Face Databases, and Lena images. The results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%. The RIFDS algorithm outperforms previous methods like Viola-Jones, Multi-block Local Binary Pattern (MBLBP), and Polar Harmonic Transforms (PHTs). The RIFDS approach has a further scope with a genetic algorithm to detect faces (approximation) even from shadows.

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Keywords

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