AI based SVM: A novel method for emotion prediction and analysis | IJCT Volume 13 – Issue 4 | IJCT-V13I4P9
International Journal of Computer Techniques
ISSN 2394-2231
Author
Saroj Kumari, Dr. Ratnesh Pathak
Abstract
Human faces display a variety of emotions that highlight the person’s mood and feelings. If work is to be provided in accordance with an individual’s mood following emotion detection, facial expression recognition plays a crucial role in the productivity output. This paper examines the use of support vector machines, a type of artificial intelligence approach, to predict basic facial expressions like happiness, sadness, and rage.
Because of its superior classification accuracy and ability to handle high-dimensional data, the SVM classifier is utilized. The suggested approach offers encouraging results in emotion classification, according to experimental study.
The Yale Dataset, which includes a variety of facial image sets with different expressions, is utilized.
The test data is fed into a support vector machine classifier using the Orange tools, which extracts features from photos that correspond to the fundamental emotions of human faces. Support Vector Machines perform better.
Keywords
Human Emotion, Facial expression, Image processing, Facial Expression, Support Vector Machine.
Conclusion
In this paper we have attempted to introduce a technique that is capable of perceives and characterizing facial feelings utilizing wide methods of images handling. It very well may be utilized to distinguish one more arrangement of articulations by changing the train dataset from which it tends to be remade. Expanding the quantity of images in the preparation dataset, its related marks in the Support Vector Machine, the various articulations grouped here can be expanded.
It comprehends a greatest acknowledgment achievement which dependent on set of 12 arrangements of facial expression images. This outcome is from a bunch of images from the Yale dataset. Utilizing more number of images in the preparation dataset there is an expansion in execution. Because of as far as possible, the quantity of train dataset images was restricted.
References
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How to Cite This Paper
Saroj Kumari, Dr. Ratnesh Pathak (2026). AI based SVM: A novel method for emotion prediction and analysis. International Journal of Computer Techniques, 13(4). ISSN: 2394-2231.
