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A deep learning approach for human face sentiment classification

This paper presents the development of a deep learning approach for human face sentiment classification. Bidirectional Long-Short Term Memory (Bi-LSTM) and recurrent neural networks (RNNs) are used for object-based segmentation in images, and CNN-RNN model is adopted for non-linear mapping. To test the applicability of this proposed approach, we have trained several deep neural networks to recognize facial expressions in 10,000 images. Our experiments show that the proposed approach can achieve an accuracy of 99.12% in classifying human face sentiment. Moreover, results indicate that the model not only boosts the classification model but also lessen the overhead.

History

Start Page

28

End Page

32

Number of Pages

5

Start Date

29/01/2021

Finish Date

30/01/2021

ISBN-13

9781665418430

Location

Ho Chi Minh City, Vietnam

Publisher

IEEE

Place of Publication

Piscataway, NJ

Peer Reviewed

Yes

Open Access

No

Author Research Institute

Centre for Intelligent Systems

Era Eligible

Yes

Name of Conference

21st ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing

Parent Title

Proceedings: 2021 21st ACIS International Semi-Virtual Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2021