Improved Face Recognition With Fractal-Based Texture Analysis | IGI Global Scientific Publishing
Welcome to the InfoSci Platform
Reference Hub2
Improved Face Recognition With Fractal-Based Texture Analysis

Improved Face Recognition With Fractal-Based Texture Analysis

Rajalaxmi Padhy (College of Engineering and Technology, Bhubaneswar, India), Aishwarya Dash (College of Engineering and Technology, Bhubaneswar, India), Sanjit Kumar Dash (College of Engineering and Technology, Bhubaneswar, India), and Jibitesh Mishra (College of Engineering and Technology, Bhubaneswar, India)
Copyright: © 2021 |Volume: 11 |Issue: 3 |Pages: 13
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781799862048|DOI: 10.4018/IJCVIP.2021070103
Cite Article Cite Article

MLA

Padhy, Rajalaxmi, et al. "Improved Face Recognition With Fractal-Based Texture Analysis." IJCVIP vol.11, no.3 2021: pp.41-53. https://doi.org/10.4018/IJCVIP.2021070103

APA

Padhy, R., Dash, A., Dash, S. K., & Mishra, J. (2021). Improved Face Recognition With Fractal-Based Texture Analysis. International Journal of Computer Vision and Image Processing (IJCVIP), 11(3), 41-53. https://doi.org/10.4018/IJCVIP.2021070103

Chicago

Padhy, Rajalaxmi, et al. "Improved Face Recognition With Fractal-Based Texture Analysis," International Journal of Computer Vision and Image Processing (IJCVIP) 11, no.3: 41-53. https://doi.org/10.4018/IJCVIP.2021070103

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Fractals are useful to uniquely represent texture in the human face, which serves as an equivalent of human vision. FaceNet, calculating face descriptors of a person, has been observed to perform with setbacks when several factors of occlusion are present. This paper proposes a new methodology that exploits the self-similar patterns in a person's face to highlight and enhance regions of high texture in a facial image. The system maps the original image into a representation in the pre-processing stage of computer vision. This representation when fed as an input to the FaceNet CNN optimizes the face embedding generated. An SVM classifier separates the hard positive examples from the hard negative examples during classification. The model is trained using YouTube Faces DB as primary dataset and for validation; a custom dataset is designed to verify a person's identity despite the presence of secondary factors such as expressions and forgery. The proposed model attained an overall accuracy of 96.73% with the YouTube Faces DB, and a notable reduction in the false positive rates is observed.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global Scientific Publishing bookstore.