Texture-based feature using multi-blocks gray level co-occurrence matrix for ethnicity identification
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. Unfortun...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IOP Publishing
2020
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/29745/ http://umpir.ump.edu.my/id/eprint/29745/1/30.%20Texture-based%20feature%20using%20multi-blocks%20gray%20level.pdf |
| _version_ | 1848823352420990976 |
|---|---|
| author | Mohd Zamri, Osman M. A., Maarof Mohd Foad, Rohani |
| author_facet | Mohd Zamri, Osman M. A., Maarof Mohd Foad, Rohani |
| author_sort | Mohd Zamri, Osman |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T02:55:46Z |
| format | Conference or Workshop Item |
| id | ump-29745 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:55:46Z |
| publishDate | 2020 |
| publisher | IOP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-297452020-11-19T07:54:54Z http://umpir.ump.edu.my/id/eprint/29745/ Texture-based feature using multi-blocks gray level co-occurrence matrix for ethnicity identification Mohd Zamri, Osman M. A., Maarof Mohd Foad, Rohani QA76 Computer software 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. IOP Publishing 2020 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/29745/1/30.%20Texture-based%20feature%20using%20multi-blocks%20gray%20level.pdf 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 (Published) https://doi.org/10.1088/1757-899X/769/1/012032 |
| spellingShingle | QA76 Computer software Mohd Zamri, Osman M. A., Maarof Mohd Foad, Rohani Texture-based feature using multi-blocks gray level co-occurrence matrix for ethnicity identification |
| title | Texture-based feature using multi-blocks gray level co-occurrence matrix for ethnicity identification |
| title_full | Texture-based feature using multi-blocks gray level co-occurrence matrix for ethnicity identification |
| title_fullStr | Texture-based feature using multi-blocks gray level co-occurrence matrix for ethnicity identification |
| title_full_unstemmed | Texture-based feature using multi-blocks gray level co-occurrence matrix for ethnicity identification |
| title_short | Texture-based feature using multi-blocks gray level co-occurrence matrix for ethnicity identification |
| title_sort | texture-based feature using multi-blocks gray level co-occurrence matrix for ethnicity identification |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/29745/ http://umpir.ump.edu.my/id/eprint/29745/ http://umpir.ump.edu.my/id/eprint/29745/1/30.%20Texture-based%20feature%20using%20multi-blocks%20gray%20level.pdf |