.png){kind=icon}
.png){kind=icon}
Return
Genetic algorithm based local and global spectral features extraction for ear recognition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2020.113639.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Identification systems based on biometric features are becoming increasingly important. One of the most common biometric features is the ear. The accuracy of these systems is heavily dependent on the characteristics extracted from them. In this paper, an appropriate combination of local and global features in the frequency domain is extracted as unique features of the ear region. In the proposed approach, at first the image quality is improved by Contrast-limited Adaptive Histogram Equalization. Then, the global features of the ear region are extracted by applying the Gabor-Zernike operator to the whole image and its non-overlapping blocks. In addition, to extract of local features, the local phase quantization operator is used on the original image of the ear region. Then, the optimum combination of global and local features is selected using Genetic Algorithm. Finally, the nearest neighbor classifier with Canberra distance is used to identify users. The proposed approach is evaluated using three databases, i.e. USTB-1, IIT125 and IIT221. The recognition rate of 100%, 99.2% and 97.13%, is reported on these databases, respectively. The obtained results show that the proposed approach performs better than existing ear recognition methods. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Biometric
Ear recognition
Feature selection
Genetic algorithm
Local and global spectral features extraction
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W