Department of Mechanical and Industrial Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia.
Sci Rep. 2024 Jun 23;14(1):14429. doi: 10.1038/s41598-024-65276-x.
Detecting emotions from facial images is difficult because facial expressions can vary significantly. Previous research on using deep learning models to classify emotions from facial images has been carried out on various datasets that contain a limited range of expressions. This study expands the use of deep learning for facial emotion recognition (FER) based on Emognition dataset that includes ten target emotions: amusement, awe, enthusiasm, liking, surprise, anger, disgust, fear, sadness, and neutral. A series of data preprocessing was carried out to convert video data into images and augment the data. This study proposes Convolutional Neural Network (CNN) models built through two approaches, which are transfer learning (fine-tuned) with pre-trained models of Inception-V3 and MobileNet-V2 and building from scratch using the Taguchi method to find robust combination of hyperparameters setting. The proposed model demonstrated favorable performance over a series of experimental processes with an accuracy and an average F1-score of 96% and 0.95, respectively, on the test data.
从面部图像中检测情绪是困难的，因为面部表情可能会有很大的变化。以前使用深度学习模型从面部图像中分类情绪的研究是在各种数据集上进行的，这些数据集只包含有限范围的表情。本研究扩展了基于 Emognition 数据集的深度学习在面部情绪识别 (FER) 中的应用，该数据集包含十种目标情绪：愉快、敬畏、热情、喜欢、惊讶、愤怒、厌恶、恐惧、悲伤和中性。进行了一系列的数据预处理，将视频数据转换为图像并扩充数据。本研究提出了两种卷积神经网络 (CNN) 模型构建方法，分别是使用 Inception-V3 和 MobileNet-V2 的预训练模型进行迁移学习（微调）和使用田口法构建从 scratch，以找到超参数设置的稳健组合。在所提出的模型的一系列实验过程中，在测试数据上的准确率和平均 F1 分数分别为 96%和 0.95。
