Group-based emotion recognition (GER) is an interesting topic in both security and social area. In this paper, a GER with hybrid optimization based recurrent fuzzy neural network is proposed which is from video sequence. In our work, by utilizing the Neural Network the emotion recognition (ER) is performed from group of people. Initially, original video frames are taken as input and pre-process it from multi user video data. From this pre-processed image, the feature extraction is done by Multivariate Local Texture Pattern (MLTP), gray-level co-occurrence matrix (GLCM), and Local Energy based Shape Histogram (LESH). After extracting the features, certain features are selected using Modified Sea-lion optimization algorithm process. Finally, recurrent fuzzy neural network (RFNN) classifier based Social Ski-Driver (SSD) optimization algorithm is proposed for classification process, SSD is used for updating the weights in the RFNN. Python platform is utilized to implement this work and the performance of accuracy, sensitivity, specificity, recall and precision is evaluated with some existing techniques. The proposed method accuracy is 99.16%, recall is 99.33%, precision is 99%, sensitivity is 99.93% and specificity is 99% when compared with other deep learning techniques our proposed method attains good result.

中文翻译：

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基于混合优化的递归模糊神经网络的视频序列群识别

基于群组的情绪识别（GER）在安全和社交领域都是一个有趣的话题。从视频序列出发，提出一种基于混合优化的递归模糊神经网络的GER。在我们的工作中，通过利用神经网络，从一群人中进行情感识别（ER）。最初，原始视频帧被用作输入，并从多用户视频数据中对其进行预处理。从该预处理图像中，通过多元局部纹理图案（MLTP），灰度共现矩阵（GLCM）和基于局部能量的形状直方图（LESH）进行特征提取。提取特征后，使用改进的海狮优化算法过程选择某些特征。最后，提出了基于递归模糊神经网络（RFNN）分类器的社交滑雪驱动器（SSD）优化算法进行分类，并使用SSD更新了RFNN中的权重。利用Python平台来实现这项工作，并使用一些现有技术来评估准确性，敏感性，特异性，召回率和精确度的性能。与其他深度学习技术相比，该方法的准确率为99.16％，召回率为99.33％，精度为99％，灵敏度为99.93％，特异性为99％。