| Autor: |
Depuru, Sivakumar, Nandam, Anjana, Ramesh, P. A., Saktivel, M., Amala, K., Sivanantham |
| Předmět: |
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| Zdroj: |
Journal of Pharmaceutical Negative Results; 2022, Vol. 13 Issue 4, p1031-1035, 5p |
| Abstrakt: |
Now a day's automatic emotion recognition system plays a vital role to recognize the human expressions. There are numerous applications available ranging from surveillance cameras to detect the emotions. Emotion recognition is an important task in emotion detection deep learning techniques are used for facial recognition. Images are used as input, and facial expressions are produced as output, such as happy, sad, disgusted, angry, fearful, surprised, and neutral. In this paper, we design a deep Convolutional Neural Network (DCNN) model. This model can be classifies seven various human facial emotions. This DCNN model is trained and tested using the FER (Facial Expression Recognition) Data set. The deep FER is analysing the methods are very difficulties. The dataset used for experimentation is FER challenge dataset available in KAGGLE repository. The implementation environment includes keras, tensorflow, and Open cv2 python packages. The results include the comparison of accuracy of emotion detection between training and testing phase. The average accuracy achieved was 86.05%. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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