Analysis of Facial Emotion Detection Using Haar-Cascade Classifier and Convolutional Neural Networks

  Rajashree Baban Kale,  Surabhi Tankkar

The detection of human face emotions is a difficult task for computer vision. Recently, advances in computer vision and machine learning have made it possible to accurately discern emotion from video or image. In this study, we suggest employing Haar-Cascade to classify face emotion. Convolutional Neural Networks and Classifiers the FER2013 dataset is used in this experiment. We presented seven solutions based on the data acquired for the face expression recognition dataset. facial expression that has been categorized Based on epoch, the CNN model gains MSE and accuracy value. The results revealed that when the epoch value increased, the MSE value decreased, would be acquired, as well as an increase in the accuracy value. As a result, the projected CNN's algorithm has been shown to be successful at detecting facial emotion.

Keywords- Haar-Cascade, Convolutional Neural Networks, FER etc

Unique Identification Number - IJEDR2201014
Page Number(s) - 75-77
Pubished in - Volume 10 | Issue 1 | March 2022
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Rajashree Baban Kale,  Surabhi Tankkar,   "Analysis of Facial Emotion Detection Using Haar-Cascade Classifier and Convolutional Neural Networks", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.10, Issue 1, pp.75-77, March 2022, Available at :http://www.ijedr.org/papers/IJEDR2201014.pdf