Texture-based feature using multi-blocks gray level co-occurrence matrix for ethnicity identification

• Main Authors: Mohd Zamri, Osman, M. A., Maarof, Mohd Foad, Rohani
• Format: Conference or Workshop Item
• Language: English
• Published: IOP Publishing 2020
• Subjects: QA76 Computer software
• Online Access: http://umpir.ump.edu.my/id/eprint/29745/1/30.%20Texture-based%20feature%20using%20multi-blocks%20gray%20level.pdf; http://umpir.ump.edu.my/id/eprint/29745/; https://doi.org/10.1088/1757-899X/769/1/012032

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.