利用深度神经网络进行面部情绪识别:视觉几何组-16 (VGG16) 技术与数据增强以提高精度的研究

IF 0.6 Q3 MULTIDISCIPLINARY SCIENCES Pertanika Journal of Science and Technology Pub Date : 2024-08-08 DOI:10.47836/pjst.32.5.02
Sarthak Kapaliya, Debabrata Swain, Ritu Sharma, Kanishka Varyani, Jyoti Thakar
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引用次数: 0

摘要

情绪在语言和非语言交流中都扮演着重要角色。面部情绪识别在各行各业都有应用,通过检测学生的表情,我们可以获得有关学生活跃程度的实时反馈。在本文中,我们旨在提供一种改进的深度学习技术,通过使用公开可用的数据集来进行情绪检测。为了获取更多数据来改善机器学习模型,我们使用了 TensorFlow 框架来增强数据。Visual Geometry Group-16 (VGG16) 是一个深度为 16 层的卷积神经网络。为了获得更好的分类结果,我们对默认的 VGG16 结构进行了修改。各种优化算法和损失函数提高了模型的准确性。我们使用了许多技术方面的评估参数,如精确度、准确度、召回率、接收者工作特征曲线下面积(AUC)和 F1 分数。建议模型的准确率为 89%,而分类精度为 81%。我们的 F1 得分为 0.42,ROC 曲线下面积(AUC)为 0.734。总体而言,该模型有利于对积极和消极情绪进行分析和分类,这将有助于检测压力、焦虑和职业倦怠的迹象,并采取预防措施来提高幸福感。
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Facial Emotion Recognition with Deep Neural Network: A Study of Visual Geometry Group-16 (VGG16) Technique with Data Augmentation for Improved Precision
Emotions play a significant role in both verbal and nonverbal communication. Facial emotion recognition has applications in various sectors where we can get real-time feedback about student activeness by detecting their expression. In this paper, we aim to provide an improved deep-learning technique to detect emotions by using publicly available datasets to perform this detection. To get more data for the well-being of the Machine Learning Model, we have used data augmentation using the TensorFlow framework. Visual Geometry Group-16 (VGG16) is a convolutional neural network of 16 layers deep. There has been an alteration to the default VGG16 structure to get better classification results. Various optimization algorithms and loss functions increase the model’s accuracy. We have used many evaluation parameters from the technical side, like precision, accuracy, recall, Area Under the Receiver Operating Characteristic Curve (AUC), and F1 Score. The proposed model has an accuracy of 89% while having a precision of 81 percent for classification. We have achieved an F1 Score of 0.42 and an area under the ROC curve (AUC) of 0.734. Overall, it would be beneficial for analyzing and categorizing positive and negative emotions, which would aid in detecting signs of stress, anxiety, and burnout, as well as taking preventive actions to enhance well-being.
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来源期刊
Pertanika Journal of Science and Technology
Pertanika Journal of Science and Technology MULTIDISCIPLINARY SCIENCES-
CiteScore
1.50
自引率
16.70%
发文量
178
期刊介绍: Pertanika Journal of Science and Technology aims to provide a forum for high quality research related to science and engineering research. Areas relevant to the scope of the journal include: bioinformatics, bioscience, biotechnology and bio-molecular sciences, chemistry, computer science, ecology, engineering, engineering design, environmental control and management, mathematics and statistics, medicine and health sciences, nanotechnology, physics, safety and emergency management, and related fields of study.
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