Identification of Batak Ethnic Groups Based on Facial Features Using Convolutional Neural Network Algorithm
Keywords:
Batak ethnicity; convolutional neural network; deep learning; face classification; ResNet50Abstract
Facial-based ethnic identification is a challenging task in computer vision due to subtle inter-class variations and high intra-class similarities. The Batak ethnic group in Indonesia exhibits distinctive facial characteristics that can be analyzed using deep learning approaches. This study aims to identify Batak and Non-Batak ethnic groups based on facial features using a Convolutional Neural Network (CNN) and a pretrained ResNet50 model. The research process includes image preprocessing, feature extraction, model training, and performance evaluation. Model performance is assessed using accuracy and confusion matrix analysis. Experimental results show that the CNN model achieved an accuracy of 67%, outperforming the ResNet50 model which obtained an accuracy of 50%. These findings indicate that simpler CNN architectures can be more effective than deep pretrained models when applied to limited and locally collected facial datasets.
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References
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