A Comparative Analysis of Emotion Detection Techniques
DOI:
https://doi.org/10.54060/jieee.2023.98Keywords:
Haar Cascade Classifiers, Emotion detection, CNN, Random Forest, Magnetic Resonance Imaging; Deep Residual Network (RESNET); glioma tumor; meningioma tumor;pituitary tumor; Deep Learning.Abstract
Emotion recognition from facial expressions has become an urgent necessity due to its numerous applications in artificial intelligence, such as human-computer interface, marketing, mental health screening, and sentiment analysis, to name a few areas where emotion detection has become essential. In this paper we present a compara-tive analysis that offers insightful information about two techniques in emotion detection with CK+ and FER2013 datasets in deep learning, assisting researchers, practitioners, and policymakers in making defensible decisions about the selection and application of different methods in diverse applications. It emphasizes how important it is to continue researching and developing in the field of emotion detection in order to make it more reliable, accurate, and equitable in a variety of real-world situations. We focused on the two emotion detection techniques and databases employed, and the contributions that were dealt with. The Cascade Classifier algorithm and the Random Forest technique are thoroughly compared in this research to provide light on their advantages, disadvantages, and suitability for use in various fields. Additionally, the study evaluates the performance of both the Cascade Classifier and Random Forest algorithm on FER2013 and CK+ datasets, considering metrics such as accuracy, precision, f1-score, etc. Finally, the assessment of these methods incorporating the review measures is reported and discussed.
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