An evaluation of ear biometric system based on enhanced Jaya algorithm and SURF descriptors

Recently, the ear biometric has received much attention for human recognition due to its unique shape and rich local features. However, extracting discriminative features from ear images is a crucial task in presence of illumination changes, low contrast, noise, and pose variations. With the aim of...

Full description

Saved in:
Bibliographic Details
Published in:Evolutionary intelligence 2020-09, Vol.13 (3), p.443-461
Main Authors: Sarangi, Partha Pratim, Mishra, Bhabani Shankar Prasad, Dehuri, Satchidanand, Cho, Sung-Bae
Format: Article
Language:English
Subjects:
Algorithms
Applications of Mathematics
Artificial Intelligence
Bioinformatics
Biometrics
Control
Ear
Engineering
Feature extraction
Heuristic
Heuristic methods
Image contrast
Image enhancement
Mathematical and Computational Engineering
Mechatronics
Mutation
Optimization
Optimization techniques
Performance evaluation
Research Paper
Robotics
Statistical Physics and Dynamical Systems
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Recently, the ear biometric has received much attention for human recognition due to its unique shape and rich local features. However, extracting discriminative features from ear images is a crucial task in presence of illumination changes, low contrast, noise, and pose variations. With the aim of neutralizing the effect of these factors, this paper proposes an automatic enhancement technique using meta-heuristic optimization to enhance the ear images. Here, we modified a recent and simple yet meta-heuristic optimization technique known as Jaya algorithm by introducing a mutation operator to enhance the ear images in few iterations and the proposed approach is named as enhanced Jaya algorithm. Then, we employed a pose-invariant local feature extractor, SURF to extract local features. Finally, the k - NN classifier has used to evaluate the rate of correct identification. Extensive experiments are conducted on four standard datasets and the performance evaluation is carried out by qualitative and quantitative measures. Experimental results clearly indicate the proposed enhancement approach is competitive as compared to two classical methods HE, CLAHE, and two meta-heuristic algorithms PSO and DE-based image enhancement techniques.
ISSN:1864-5909
1864-5917
DOI:10.1007/s12065-019-00311-9