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Facial emotion recognition using emotional neural network and hybrid of fuzzy c-means and genetic algorithm

机译:基于情感神经网络的人脸情感识别及模糊c均值与遗传算法的混合

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Facial emotion recognition (FER) is a critical task for both human-human (HHI) and human-computer interactions (HCl). In this paper, a brain-inspired neural basis computational model of FER is proposed based on emotional neural networks (ENN), fuzzy c-means (FCM) and genetic algorithms (GA). The proposed model can be applied in both HHI and HCI applications. In HHI, it can be used for improving communication skills, and in HCI it can be used in various treatment processes e.g. anxiety treatment, cancer radiation treatment and remote children/elderlies monitoring systems. The proposed model consists of main modules of emotional brain which recognize the facial emotions. In the experimental studies, the proposed model is examined on children's facial sad recognition as a case study. The results show that our model is valid and can be applied for various FER tasks.
机译:面部表情识别(FER)是人与人(HHI)和人机交互(HCl)的关键任务。本文基于情感神经网络(ENN),模糊c均值(FCM)和遗传算法(GA)提出了FER的脑启发式神经基础计算模型。所提出的模型可以同时应用于HHI和HCI应用中。在HHI中,它可以用于改善沟通技巧,在HCI中,它可以用于各种治疗过程,例如焦虑症治疗,癌症放射治疗和远程儿童/老人监护系统。所提出的模型由识别面部表情的情绪大脑的主要模块组成。在实验研究中,以儿童面部悲伤识别作为案例研究了所提出的模型。结果表明,我们的模型是有效的,可以应用于各种FER任务。

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