The Improved Artificial Neural Network Based on Cosine Similarity in Facial Emotion Recognition

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Kartika Candra Kirana ◽  
Slamet Wibawanto ◽  
Nur Hidayah ◽  
Gigih Prasetyo Cahyono
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Emotion Recognition ◽  
Cosine Similarity ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Artificial Neural

scholarly journals Make Patient Consultation Warmer: A Clinical Application for Speech Emotion Recognition

2021 ◽  
Vol 11 (11) ◽  
pp. 4782
Author(s):  
Huan-Chung Li ◽  
Telung Pan ◽  
Man-Hua Lee ◽  
Hung-Wen Chiu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Emotion Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Speech Emotion Recognition ◽  
Single Observation ◽  
Artificial Neural

In recent years, many types of research have continued to improve the environment of human speech and emotion recognition. As facial emotion recognition has gradually matured through speech recognition, the result of this study provided more accurate recognition of complex human emotional performance, and speech emotion identification will be derived from human subjective interpretation into the use of computers to automatically interpret the speaker’s emotional expression. Focused on use in medical care, which can be used to understand the current feelings of physicians and patients during a visit, and improve the medical treatment through the relationship between illness and interaction. By transforming the voice data into a single observation segment per second, the first to the thirteenth dimensions of the frequency cestrum coefficients are used as speech emotion recognition eigenvalue vectors. Vectors for the eigenvalue vectors are maximum, minimum, average, median, and standard deviation, and there are 65 eigenvalues in total for the construction of an artificial neural network. The sentiment recognition system developed by the hospital is used as a comparison between the sentiment recognition results of the artificial neural network classification, and then use the foregoing results for a comprehensive analysis to understand the interaction between the doctor and the patient. Using this experimental module, the emotion recognition rate is 93.34%, and the accuracy rate of facial emotion recognition results can be 86.3%.

scholarly journals Real-time facial emotion recognition using lightweight convolution neural network

2021 ◽  
Vol 1827 (1) ◽  
pp. 012130
Author(s):  
Qi Li ◽  
Yun Qing Liu ◽  
Yue Qi Peng ◽  
Cong Liu ◽  
Jun Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Real Time ◽  
Convolution Neural Network ◽  
Facial Emotion Recognition ◽  
Facial Emotion

scholarly journals Efficient Facial Emotion Recognition Model using Deep Convolutional Neural Network and Modified Joint Trilateral Filter

2021 ◽  
Author(s):  
Naveen Kumari ◽  
Rekha Bhatia
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Deep Convolutional Neural Network ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Learning Models ◽  
Recognition Model ◽  
Trilateral Filter

Abstract Facial emotion recognition extracts the human emotions from the images and videos. As such, it requires an algorithm to understand and model the relationships between faces and facial expressions, and to recognize human emotions. Recently, deep learning models are extensively utilized enhance the facial emotion recognition rate. However, the deep learning models suffer from the overfitting issue. Moreover, deep learning models perform poorly for images which have poor visibility and noise. Therefore, in this paper, a novel deep learning based facial emotion recognition tool is proposed. Initially, a joint trilateral filter is applied to the obtained dataset to remove the noise. Thereafter, contrast-limited adaptive histogram equalization (CLAHE) is applied to the filtered images to improve the visibility of images. Finally, a deep convolutional neural network is trained. Nadam optimizer is also utilized to optimize the cost function of deep convolutional neural networks. Experiments are achieved by using the benchmark dataset and competitive human emotion recognition models. Comparative analysis demonstrates that the proposed facial emotion recognition model performs considerably better compared to the competitive models.

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