论文标题
基于最佳卷积神经网络（MFEOCNN）算法的视频中的微种族表达识别
Micro-Facial Expression Recognition in Video Based on Optimal Convolutional Neural Network (MFEOCNN) Algorithm
论文作者
论文摘要
面部表达是人类情感识别最重要的特征之一。人们利用了人们的表情。无论如何，对面部表情的认识一直持续存在关于PC视觉的测试和有趣的问题。在视频序列中识别微种族表达是提出方法的主要目标。为了有效识别，提出的方法利用了最佳卷积神经网络。在这里，考虑输入数据集的建议方法是CK+数据集。首先，通过自适应中值滤波，在输入图像中进行预处理。从预处理的输出中，提取的特征是几何特征，定向梯度特征的直方图和局部二进制图案特征。该方法的新颖性是，借助改良的狮子优化（MLO）算法，从提取的特征中选择了最佳特征。在较短的计算时间内，它具有快速关注并有效承认的好处，目的是获得整体安排或想法。最后，识别是由卷积神经网络（CNN）完成的。然后根据错误的度量和识别准确性分析了所提出的Mfeocnn方法的性能。这种情绪识别主要用于医学，营销，电子学习，娱乐，法律和监测。从模拟中，我们知道所提出的方法以最小平均绝对误差（MAE）值获得了99.2％的最大识别精度。将这些结果与基于微生物表达的深根学习（MFEDRL），具有狮子优化的卷积神经网络（CNN+LO）和卷积神经网络（CNN）进行比较。该方法的仿真是在MATLAB的工作平台中进行的。
Facial expression is a standout amongst the most imperative features of human emotion recognition. For demonstrating the emotional states facial expressions are utilized by the people. In any case, recognition of facial expressions has persisted a testing and intriguing issue with regards to PC vision. Recognizing the Micro-Facial expression in video sequence is the main objective of the proposed approach. For efficient recognition, the proposed method utilizes the optimal convolution neural network. Here the proposed method considering the input dataset is the CK+ dataset. At first, by means of Adaptive median filtering preprocessing is performed in the input image. From the preprocessed output, the extracted features are Geometric features, Histogram of Oriented Gradients features and Local binary pattern features. The novelty of the proposed method is, with the help of Modified Lion Optimization (MLO) algorithm, the optimal features are selected from the extracted features. In a shorter computational time, it has the benefits of rapidly focalizing and effectively acknowledging with the aim of getting an overall arrangement or idea. Finally, the recognition is done by Convolution Neural network (CNN). Then the performance of the proposed MFEOCNN method is analysed in terms of false measures and recognition accuracy. This kind of emotion recognition is mainly used in medicine, marketing, E-learning, entertainment, law and monitoring. From the simulation, we know that the proposed approach achieves maximum recognition accuracy of 99.2% with minimum Mean Absolute Error (MAE) value. These results are compared with the existing for MicroFacial Expression Based Deep-Rooted Learning (MFEDRL), Convolutional Neural Network with Lion Optimization (CNN+LO) and Convolutional Neural Network (CNN) without optimization. The simulation of the proposed method is done in the working platform of MATLAB.
