Mohd Zamri, Osman and M. A., Maarof and Mohd Foad, Rohani (2020) Texture-based feature using multi-blocks gray level co-occurrence matrix for ethnicity identification. In: IOP Conference Series: Materials Science and Engineering, The 6th International Conference on Software Engineering & Computer Systems , 25-27 September 2019 , Pahang, Malaysia. pp. 1-7., 769 (012032). ISSN 1757-899X
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Abstract
Ethnicity identification for demographic information has been studied for soft biometric analysis, and it is essential for human identification and verification. Ethnicity identification remains popular and receives attention in a recent year especially in automatic demographic information. Unfortunately, ethnicity identification in a multi-class which consist of several ethnic classes may degrade the accuracy of the ethnic identification. Thus, this paper purposely analyses the accuracy of the texture-based ethnicity identification model from facial components under four-class ethnics. The proposed model involved several phases such as face detection, feature selection, and classification. The detected face then exploited by three proposed face block which are 1×1, 1×2 and 2×2. In the feature extraction process, a Grey Level Co-occurrence Matrix (GLCM) under different face blocks were employed. Then, final stage was undergone with several classification algorithms such as Naïve Bayes, BayesNet, kNearest Neighbour (k-NN), Random Forest, and Multilayer Perceptron (MLP). From the experimental result, we achieved a better result 2×2 face block feature compared to 1×1 and 2×2 feature representation under Random Forest algorithm.
| Item Type: | Conference or Workshop Item (Lecture) |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Multilayer Perceptron (MLP).Grey Level Co-occurrence Matrix (GLCM); k- Nearest Neighbour (k-NN) |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Faculty/Division: | Faculty of Computing |
| Depositing User: | Pn. Hazlinda Abd Rahman |
| Date Deposited: | 19 Nov 2020 07:54 |
| Last Modified: | 19 Nov 2020 07:54 |
| URI: | http://umpir.ump.edu.my/id/eprint/29745 |
| Download Statistic: | View Download Statistics |
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