Facial Expression Recognition using Convolutional Neural Networks with Transfer Learning Resnet-50

Annisa Ayu Istiqomah; Christy Atika Sari; Ajib Susanto; Eko Hari Rachmawanto

doi:10.30871/jaic.v8i2.8329

Annisa Ayu Istiqomah Universitas Dian Nuswantoro
Christy Atika Sari Universitas Dian Nuswantoro https://orcid.org/0000-0002-7296-5210
Ajib Susanto Universitas Dian Nuswantoro
Eko Hari Rachmawanto Universitas Dian Nuswantoro https://orcid.org/0000-0001-6014-1903

DOI: https://doi.org/10.30871/jaic.v8i2.8329

Keywords: Convolutional Neural Networks, Facial Expression Recognition, Image Classification, Resnet-50, Transfer Learning

Abstract

Facial expression recognition is important for many applications, including sentiment analysis, human-computer interaction, and interactive systems in areas such as security, healthcare, and entertainment. However, this task is fraught with challenges, mainly due to large differences in lighting conditions, viewing angles, and differences in individual eye structures. These factors can drastically affect the appearance of facial expressions, making it difficult for traditional recognition systems to consistently and accurately identify emotions. Variations in lighting can alter the visibility of facial features, while different angles can obscure critical details necessary for accurate expression detection. This study addresses these issues by employing transfer learning with ResNet-50 and effective pre-processing techniques. The dataset consists of grayscale images with a 48 x 48 pixels resolution. It includes a total of 680 samples categorized into seven classes: anger, contempt, disgust, fear, happy, sadness, and surprise. The dataset was divided so that 80% was allocated for training and 20% for testing to ensure robust model evaluation. The results demonstrate that the model utilizing transfer learning achieved an exceptional performance level, with accuracy at 99.49%, precision at 99.49%, recall at 99.71%, and an F1-score of 99.60%, significantly outperforming the model without transfer learning. Future research will focus on implementing real-time facial recognition systems and exploring other advanced transfer learning models to further enhance accuracy and operational efficiency.

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Author Biographies

Christy Atika Sari, Universitas Dian Nuswantoro

Christy Atika Sari received the Master's degree in Informatic Engineering from Dian Nuswantoro University and University Teknikal Malaysia Melaka (UTeM) in 2012. She is currently active as an author and reviewer of an international journal and conference Scopus indexed. She was also awarded as best author and best paper at a national and international conference in 2019 and 2020 respectively and was awarded by the Indonesian Ministry of Education and Culture Research and Technology as the Indonesian top 50 best researchers in 2020. She’s research interest is quantum computing for security data, machine learning, deep learning, and image processing. She can be contacted at email: [email protected].

Eko Hari Rachmawanto, Universitas Dian Nuswantoro

Eko Hari Rachmawanto received a bachelor's degree in Informatic Engineering from the University of Dian Nuswantoro, in 2010. He received a master's double degree University of Dian Nuswantoro and Universiti Teknikal Malaysia Malacca (UTeM) in 2010 and 2012. Since 2012, he joined as a lecturer in Informatics Engineering at, the University of Dian Nuswantoro, Semarang, Indonesia. Now he serves as Editor in Chief of Accredited Indonesian National Journal. Since 2022 he has supervised the informatics engineering study program in study programs outside the main campus as head of the study program in Kediri, Indonesia. Now he is a member of Security Data Collaboration Research and tasked with developing several researchers regarding data security, and image processing. He can be contacted at email: [email protected].

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Facial Expression Recognition using Convolutional Neural Networks with Transfer Learning Resnet-50

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