Facial expressions are important cues to observe human emotions. Facial expression recognition has attracted many researchers for years, but it is still a challenging topic since expression features vary greatly with the head poses, environments, and variations in the different persons involved. In this work, three major steps are involved to improve the performance of micro-facial expression recognition. First, an Adaptive Homomorphic Filtering is used for face detection and rotation rectification processes. Secondly, Micro-facial features were used to extract the appearance variations of a testing image-spatial analysis. The features of motion information are used for expression recognition in a sequence of facial images. An effective Micro-Facial Expression Based Deep-Rooted Learning (MFEDRL) classifier is proposed in this paper to better recognize spontaneous micro-expressions by learning parameters on the optimal features. This proposed method includes two loss functions such as cross entropy loss function and centre loss function. Then the performance of the algorithm will be evaluated using recognition rate and false measures. Simulation results show that the predictive performance of the proposed method outperforms that of the existing classifiers such as Convolutional Neural Network (CNN), Deep Neural Network (DNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and k-Nearest Neighbours (KNN) in terms of accuracy and Mean Absolute Error (MAE).

中文翻译：

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基于深根学习算法的微表情识别

面部表情是观察人类情绪的重要线索。面部表情识别多年来吸引了许多研究人员，但它仍然是一个具有挑战性的课题，因为表情特征会随着头部姿势、环境和不同人的变化而有很大差异。在这项工作中，涉及三个主要步骤来提高微面部表情识别的性能。首先，自适应同态滤波用于人脸检测和旋转校正过程。其次，微面部特征用于提取测试图像空间分析的外观变化。运动信息的特征用于面部图像序列中的表情识别。本文提出了一种有效的基于微面部表情的深根学习（MFEDRL）分类器，通过学习最优特征的参数来更好地识别自发的微表情。该方法包括交叉熵损失函数和中心损失函数两个损失函数。然后将使用识别率和错误度量来评估算法的性能。仿真结果表明，该方法的预测性能优于现有分类器，如卷积神经网络 (CNN)、深度神经网络 (DNN)、人工神经网络 (ANN)、支持向量机 (SVM) 和 k-最近邻 (KNN) 在准确性和平均绝对误差 (MAE) 方面。该方法包括交叉熵损失函数和中心损失函数两个损失函数。然后将使用识别率和错误度量来评估算法的性能。仿真结果表明，该方法的预测性能优于现有分类器，如卷积神经网络 (CNN)、深度神经网络 (DNN)、人工神经网络 (ANN)、支持向量机 (SVM) 和 k-最近邻 (KNN) 在准确性和平均绝对误差 (MAE) 方面。该方法包括交叉熵损失函数和中心损失函数两个损失函数。然后将使用识别率和错误度量来评估算法的性能。仿真结果表明，该方法的预测性能优于现有分类器，如卷积神经网络 (CNN)、深度神经网络 (DNN)、人工神经网络 (ANN)、支持向量机 (SVM) 和 k-最近邻 (KNN) 在准确性和平均绝对误差 (MAE) 方面。