Abstract
Facial expression recognition (FER) has attracted the interest of many scholars because it plays an important role in human-computer interaction, image analysis, and artificial intelligent. The main purpose of FER is to classify a given facial image into one of the seven basic emotions: angry, disgust, fear, happy, sad, surprise, and neutral. In recent years, convolutional neural networks (CNN) have been studied and applied in the fields of image processing and computer vision a with great success. One of the main properties of CNN is the training stage that needs a large-scale data set for having a good performance. In this paper, we present a FER system using CNN in which the training and testing images are extracted from the AffectNet facial expression database. Compared to the traditional facial expression databases, AffectNet provides over one million images which are annotated by manual and automatic methods. The performance of the proposed model is analyzed via evaluations of the correct recognition rates, in comparison with the published ones, with the use of the same database.
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Do, H.N. et al. (2020). Automatic Facial Expression Recognition System Using Convolutional Neural Networks. In: Van Toi , V., Le, T., Ngo, H., Nguyen, TH. (eds) 7th International Conference on the Development of Biomedical Engineering in Vietnam (BME7). BME 2018. IFMBE Proceedings, vol 69. Springer, Singapore. https://doi.org/10.1007/978-981-13-5859-3_82
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DOI: https://doi.org/10.1007/978-981-13-5859-3_82
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