论文标题
基于深根学习算法的微观表达识别
Micro-Facial Expression Recognition Based on Deep-Rooted Learning Algorithm
论文作者
论文摘要
面部表情是观察人情绪的重要提示。多年来，面部表情识别吸引了许多研究人员，但是这仍然是一个具有挑战性的话题，因为表达功能随所涉及的不同人的头部姿势，环境和变化而异。在这项工作中，涉及三个主要步骤，以提高微种族表达识别的性能。首先，使用自适应同构滤波进行面部检测和旋转整流过程。其次，使用微种族特征来提取测试图像空间分析的外观变化。运动信息的特征用于以一系列面部图像的序列进行表达识别。本文提出了一种有效的基于微种族表达的深度学习（MFEDRL）分类器，以通过学习最佳特征的学习参数来更好地识别自发的微表达。该提出的方法包括两个损失函数，例如跨熵损失函数和中心损失函数。然后，将使用识别率和虚假测量方法评估该算法的性能。仿真结果表明，所提出的方法的预测性能优于现有分类器，例如卷积神经网络（CNN），深神经网络（DNN），人工神经网络（ANN），支持向量机（SVM）和K-Nearest Neighbors（KNN）的准确性和平均绝对错误（MAE）。
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).
