Facial Expression Recognition with Convolutional Neural Networks
Facial Expression Recognition with Convolutional Neural Networks
复制标题
使用卷积神经网络进行面部表情识别
DOI：
10.1109/ccwc47524.2020.9031283
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Fatma Nasoz
中科院分区：
文献类型：
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作者：
Shekhar Singh;Fatma Nasoz
情绪是一种强大的沟通工具，人类表达情绪的一种方式是通过面部表情。面部表情识别是社会交际中具有挑战性和强大的任务之一，因为在非语言交际中，面部表情是关键。在人工智能领域，面部表情识别（FER）是一个活跃的研究领域，最近有一些研究使用卷积神经网络（cnn）。在本文中，我们演示了基于静态图像的FER分类，使用cnn，不需要任何预处理或特征提取任务。本文还阐述了通过预处理来提高该领域未来精度的技术，包括人脸检测和照明校正。特征提取用于提取面部最突出的部分，包括下巴、嘴巴、眼睛、鼻子和眉毛。此外，我们还讨论了文献综述，并介绍了我们的CNN架构，以及使用max-pooling和dropout的挑战，这最终有助于提高性能。在FER2013上，我们在7类分类任务中获得了61.7%的测试准确率，而在最先进的分类任务中则为75.2%。
Emotions are a powerful tool in communication and one way that humans show their emotions is through their facial expressions. One of the challenging and powerful tasks in social communications is facial expression recognition, as in non-verbal communication, facial expressions are key. In the field of Artificial Intelligence, Facial Expression Recognition (FER) is an active research area, with several recent studies using Convolutional Neural Networks (CNNs). In this paper, we demonstrate the classification of FER based on static images, using CNNs, without requiring any pre-processing or feature extraction tasks. The paper also illustrates techniques to improve future accuracy in this area by using pre-processing, which includes face detection and illumination correction. Feature extraction is used to extract the most prominent parts of the face, including the jaw, mouth, eyes, nose, and eyebrows. Furthermore, we also discuss the literature review and present our CNN architecture, and the challenges of using max-pooling and dropout, which eventually aided in better performance. We obtained a test accuracy of 61.7% on FER2013 in a seven-classes classification task compared to 75.2% in state-of-the-art classification.