A deep learning approach for human face sentiment classification

Dr Chetanpal Singh; Santoso Wibowo; Srimannarayana Grandhi

doi:10.1109/SNPDWinter52325.2021.00015

A deep learning approach for human face sentiment classification

Dr Chetanpal Singh, Santoso Wibowo, Srimannarayana Grandhi

CQ University

Research output: Book chapter/Published conference paper › Conference paper › peer-review

3 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings, 2021 21st ACIS International Semi-Virtual Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing
Subtitle of host publication	SNPD-Winter 2021
Place of Publication	United States
Publisher	IEEE
Pages	28-32
Number of pages	5
ISBN (Electronic)	9781665418430
ISBN (Print)	9781665448260 (Print on demand), 9780738125510
DOIs	https://doi.org/10.1109/SNPDWinter52325.2021.00015
Publication status	E-pub ahead of print - 16 Apr 2021
Event	21st ACIS International Semi-Virtual Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD2021-Winter): SNPD 2021 Winter - University of Economics, Ho Chi Minh City, Viet Nam Duration: 28 Jan 2021 → 30 Jan 2021 http://acisinternational.org/conferences/snpd-2021-winter/ https://ieeexplore.ieee.org/xpl/conhome/9403428/proceeding (Conference proceedings) http://acisinternational.org/wp-content/uploads/2021/01/SNPD-2021-Winter-ProgramVirtual.pdf (Conference program)

Conference

Conference	21st ACIS International Semi-Virtual Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD2021-Winter)
Country/Territory	Viet Nam
City	Ho Chi Minh City
Period	28/01/21 → 30/01/21
Other	The 21st ACIS International Semi-Virtual Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD2021-Winter) brings together researchers, scientists, engineers, industry practitioners, and students to discuss, encourage and exchange new ideas, research results, and experiences on all aspects of computer and information science. SNPD2021 Winter aims to facilitate cross-fertilizations among the key technology enabling areas and is soliciting papers in these areas.
Internet address	http://acisinternational.org/conferences/snpd-2021-winter/ https://ieeexplore.ieee.org/xpl/conhome/9403428/proceeding (Conference proceedings) http://acisinternational.org/wp-content/uploads/2021/01/SNPD-2021-Winter-ProgramVirtual.pdf (Conference program)

Access to Document

10.1109/SNPDWinter52325.2021.00015

A Deep Learning Approach for Human Face Sentiment Classification
Dr Chetanpal Singh (Speaker)
Nov 2020
Activity: Scholarly activities in Learning and Teaching reflection › Peer reviewed publication reflection

Cite this

Singh, D. C., Wibowo, S., & Grandhi, S. (2021). A deep learning approach for human face sentiment classification. In Proceedings, 2021 21st ACIS International Semi-Virtual Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing: SNPD-Winter 2021 (pp. 28-32). [20633206] IEEE. https://doi.org/10.1109/SNPDWinter52325.2021.00015

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title = "A deep learning approach for human face sentiment classification",

abstract = "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.",

keywords = "Image classification, Deep learning, Convolution neural network, Big data, Recurrent neural networks",

author = "Singh, {Dr Chetanpal} and Santoso Wibowo and Srimannarayana Grandhi",

note = "Includes bibliographical references.; 21st ACIS International Semi-Virtual Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD2021-Winter) : SNPD 2021 Winter ; Conference date: 28-01-2021 Through 30-01-2021",

year = "2021",

month = apr,

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doi = "10.1109/SNPDWinter52325.2021.00015",

language = "English",

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

publisher = "IEEE",

url = "http://acisinternational.org/conferences/snpd-2021-winter/, https://ieeexplore.ieee.org/xpl/conhome/9403428/proceeding, http://acisinternational.org/wp-content/uploads/2021/01/SNPD-2021-Winter-ProgramVirtual.pdf",

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Singh, DC, Wibowo, S & Grandhi, S 2021, A deep learning approach for human face sentiment classification. in Proceedings, 2021 21st ACIS International Semi-Virtual Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing: SNPD-Winter 2021., 20633206, IEEE, United States, pp. 28-32, 21st ACIS International Semi-Virtual Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD2021-Winter), Ho Chi Minh City, Viet Nam, 28/01/21. https://doi.org/10.1109/SNPDWinter52325.2021.00015

A deep learning approach for human face sentiment classification. / Singh, Dr Chetanpal; Wibowo, Santoso; Grandhi, Srimannarayana.
Proceedings, 2021 21st ACIS International Semi-Virtual Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing: SNPD-Winter 2021. United States: IEEE, 2021. p. 28-32 20633206.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

TY - GEN

T1 - A deep learning approach for human face sentiment classification

AU - Singh, Dr Chetanpal

AU - Wibowo, Santoso

AU - Grandhi, Srimannarayana

N1 - Includes bibliographical references.

PY - 2021/4/16

Y1 - 2021/4/16

N2 - 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.

AB - 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.

KW - Image classification

KW - Deep learning

KW - Convolution neural network

KW - Big data

KW - Recurrent neural networks

U2 - 10.1109/SNPDWinter52325.2021.00015

DO - 10.1109/SNPDWinter52325.2021.00015

M3 - Conference paper

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SN - 9780738125510

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EP - 32

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

PB - IEEE

CY - United States

T2 - 21st ACIS International Semi-Virtual Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD2021-Winter)

Y2 - 28 January 2021 through 30 January 2021

ER -

Singh DC, Wibowo S, Grandhi S. A deep learning approach for human face sentiment classification. In Proceedings, 2021 21st ACIS International Semi-Virtual Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing: SNPD-Winter 2021. United States: IEEE. 2021. p. 28-32. 20633206 Epub 2021 Apr 16. doi: 10.1109/SNPDWinter52325.2021.00015

A deep learning approach for human face sentiment classification

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A Deep Learning Approach for Human Face Sentiment Classification

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