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Automatic Ear Localization Using Entropy-Based Binary Jaya Algorithm and Weighted Hausdorff Distance

Automatic Ear Localization Using Entropy-Based Binary Jaya Algorithm and Weighted Hausdorff Distance

Partha Pratim Sarangi, Abhimanyu Sahu, Madhumita Panda, Bhabani Shankar Prasad Mishra
Copyright: © 2021 |Volume: 12 |Issue: 1 |Pages: 27
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781799861324|DOI: 10.4018/IJSIR.2021010104
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MLA

Sarangi, Partha Pratim, et al. "Automatic Ear Localization Using Entropy-Based Binary Jaya Algorithm and Weighted Hausdorff Distance." IJSIR vol.12, no.1 2021: pp.50-76. http://doi.org/10.4018/IJSIR.2021010104

APA

Sarangi, P. P., Sahu, A., Panda, M., & Mishra, B. S. (2021). Automatic Ear Localization Using Entropy-Based Binary Jaya Algorithm and Weighted Hausdorff Distance. International Journal of Swarm Intelligence Research (IJSIR), 12(1), 50-76. http://doi.org/10.4018/IJSIR.2021010104

Chicago

Sarangi, Partha Pratim, et al. "Automatic Ear Localization Using Entropy-Based Binary Jaya Algorithm and Weighted Hausdorff Distance," International Journal of Swarm Intelligence Research (IJSIR) 12, no.1: 50-76. http://doi.org/10.4018/IJSIR.2021010104

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Abstract

This paper presents an automatic human ear localization technique for handling uncontrolled scenarios such as illumination variation, poor contrast, partial occlusion, pose variation, ear ornaments, and background noise. The authors developed entropy-based binary Jaya algorithm (EBJA) and weighted doubly modified Hausdorff distance (W-MHD) to use edge information rather than pixels intensity values of the side face image. First, it embodies skin segmentation procedure using skin color model and successively remove spurious and non-ear edges which reduces the search space of the skin regions. Secondly, EBJA is proposed to trace dense edge regions as probable ear candidates. Thirdly, this paper developed an edge based weight function to represent the ear shape along with for the edge based template matching using W-MHD to identify true ear from a set of probable ear candidates. Experimental results using publicly available benchmark datasets demonstrate the competitiveness of the proposed technique in comparison to the state-of-the-art methods.

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