Facial Emotion Prediction Model using Cascade of Neural Network - A Machine Learning Perspective
DOI:
https://doi.org/10.48047/d17zpt17Keywords:
Deep Learning, Internet of Things, Convolutional Neural Networks, Facial Landmark, VGG16.Abstract
In the last two decades, research into emotion recognition has been one of the most dynamic natures. Here, Convolutional Neural Network (CNN) and an unsupervised algorithm to classify is used human emotions from real-time monitoring of the users’ emotional state while the image was being analyzed. To accomplish this, a real-time emotion identification system based on virtual markers has been implemented in this proposal. In our work, features are extracted, a subset of those features is created, and an emotions classifier is developed in three stages. The input image's identity, characteristic values, and facial features are determined with the support of the Haar Cascade approach. The VGG16 method places the virtual markers in specific positions on the recognized face. Cross-validation is then used to verify the components before sending them to the CNN classifiers. To prove
the effectiveness of our work, we have compared it with traditional machine learning classifiers as well as advanced neural networks. Compared to existing methods, the suggested model performs better in experiments and yields results appropriate for real-time facial expressions with an accuracy of 91.03%
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References
Setiawan, Feri, Aria GhoraPrabono, Sunder Ali Khowaja, Wangsoo Kim, Kyoungsoo Park, Bernardo NugrohoYahya, Seok-Lyong Lee, and JinPyo Hong. (2020) Fine-grained emotion recognition: fusion of physiological signals and facial expressions on spontaneous emotion corpus. International Journal of Ad Hoc and Ubiquitous Computing 35, no. 3,pp 162-178.
Lim, Andreas Pangestu, Gede Putra Kusuma, and Amalia Zahra, (2018) Facial emotion recognition using computer vision. In 2018 Indonesian Association for Pattern Recognition International Conference (INAPR) IEEE, pp. 46-50.
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