| Autor: |
Harish, B.S., Maheshan, M.S., Roopa, C.K., Rangan, R. Kasturi |
| Zdroj: |
International Journal of Computational Vision and Robotics; 2023, Vol. 13 Issue: 3 p304-315, 12p |
| Abstrakt: |
Among the various biometric traits that exist in the human body, sclera is considered to be prominent because of its unique characteristics. In this paper, we propose an improved sclera recognition method using kernel entropy component analysis (KECA). The main objective of this paper is to integrate kernel-based methods with entropy to choose the best principal components. Further, the resulting top principal components are given a symbolic interval valued representation. To evaluate the efficiency of the new proposed representation method, we conducted extensive experimentation using various classifiers. The proposed method has achieved over 5.09% of hike in the accuracy result with 50:50 split and over 10.69% of hike with 60:40 split, respectively. The obtained result of the proposed method is effective and feasible for sclera recognition. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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