Convolutional Neural Network With Batch Normalization for Classification of Emotional Expressions Based on Facial Images

  • Bambang Krismono Triwijoyo
  • Ahmat Adil Universitas Bumigora
  • Anthony Anggrawan Universitas Bumigora
Keywords: Convolutional Neural Network, Batch Normalization, Classification, Emotional Expressions, Facial Images


Emotion recognition through facial images is one of the most challenging topics in human psychological interactions with machines. Along with advances in robotics, computer graphics, and computer vision, research on facial expression recognition is an important part of intelligent systems technology for interactive human interfaces where each person may have different emotional expressions, making it difficult to classify facial expressions and requires training data. large, so the deep learning approach is an alternative solution., The purpose of this study is to propose a different Convolutional Neural Network (CNN) model architecture with batch normalization consisting of three layers of multiple convolution layers with a simpler architectural model for the recognition of emotional expressions based on human facial images in the FER2013 dataset from Kaggle. The experimental results show that the training accuracy level reaches 98%, but there is still overfitting where the validation accuracy level is still 62%. The proposed model has better performance than the model without using batch normalization.


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Author Biographies

Ahmat Adil, Universitas Bumigora

Computer Science Departement 

Anthony Anggrawan, Universitas Bumigora

Computer Science Departement


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How to Cite
Triwijoyo, B., Adil, A., & Anggrawan, A. (2021). Convolutional Neural Network With Batch Normalization for Classification of Emotional Expressions Based on Facial Images. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(1), 197-204.

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