论文标题
情绪识别的卷积神经网络，以协助精神科医生和心理学家在19日大流行期间：专家意见
Convolutional Neural Network for emotion recognition to assist psychiatrists and psychologists during the COVID-19 pandemic: experts opinion
论文作者
论文摘要
介绍了具有实时情感识别的Web应用程序。由于社会受到情感影响，因此需要处理Covid-19隔离期间的心理健康影响。人类的微表达可以描述可以通过卷积神经网络（CNN）模型捕获的真实情绪。但是，挑战是在社会计算机的一部分和互联网连接的低速度（即提高计算效率并降低数据传输）下实施它。为了验证计算效率前提，我们比较CNN架构结果，收集每秒（FLOPS）的浮点操作，参数数量（NP）和Mobilenet，Peleenet，Peleenet，Exted Deep Deep Neural Network（EDNN），Inception-Inception-基于基于基于的神经网络（IDNN）（IDNN）和我们建议的基于Mobile Mobiles Mobiletual Mobiletual Mostial Models（resmobial ModialeT）（Resignual Mobiletual ModialeT（Resignual Mobiletual Modelet）。此外，我们比较了受过训练的模型在主内存利用率（MMU）和响应时间方面的结果，以完成情绪（RTE）识别。此外，我们设计了一个数据传输，其中包括情绪的原始数据和基本的患者信息。通过系统可用性量表（SUS）评估Web应用程序，并由心理学家和精神科医生评估了公用事业问卷。 ResMonet模型生成了最减少的NP，FLOP和MMU结果，仅EDNN在RTE中克服了0.01 sec的ResMonet。对我们的模型的优化影响了准确性，因此IDNN和EDNN比我们的模型分别高0.02和0.05。最后，根据心理学家和精神科医生的说法，Web应用程序具有良好的可用性（100个中的73.8）和实用程序（第5件事3.94）。
A web application with real-time emotion recognition for psychologists and psychiatrists is presented. Mental health effects during COVID-19 quarantine need to be handled because society is being emotionally impacted. The human micro-expressions can describe genuine emotions that can be captured by Convolutional Neural Networks (CNN) models. But the challenge is to implement it under the poor performance of a part of society computers and the low speed of internet connection, i.e., improve the computational efficiency and reduce the data transfer. To validate the computational efficiency premise, we compare CNN architectures results, collecting the floating-point operations per second (FLOPS), the Number of Parameters (NP) and accuracy from the MobileNet, PeleeNet, Extended Deep Neural Network (EDNN), Inception- Based Deep Neural Network (IDNN) and our proposed Residual mobile-based Network model (ResmoNet). Also, we compare the trained models results in terms of Main Memory Utilization (MMU) and Response Time to complete the Emotion (RTE) recognition. Besides, we design a data transfer that includes the raw data of emotions and the basic patient information. The web application was evaluated with the System Usability Scale (SUS) and a utility questionnaire by psychologists and psychiatrists. ResmoNet model generated the most reduced NP, FLOPS, and MMU results, only EDNN overcomes ResmoNet in 0.01sec in RTE. The optimizations to our model impacted the accuracy, therefore IDNN and EDNN are 0.02 and 0.05 more accurate than our model respectively. Finally, according to psychologists and psychiatrists, the web application has good usability (73.8 of 100) and utility (3.94 of 5).
