A Literature Review on Emotion Recognition Using Various Methods

Reeshad Khan; Omar Sharif

A Literature Review on Emotion Recognition Using Various Methods

Reeshad Khan *

Contact

Omar Sharif

Contact

A Literature Review on Emotion Recognition Using Various Methods

Article Fingerprint

ReserarchID

CSTAP1X7

A Literature Review on Emotion Recognition Using Various Methods Banner

Key Research Insights

Synthesized scholarly intelligence & interactive research assistant

Abstract

Emotion Recognition is an important area of work to improve the interaction between human and machine. Complexity of emotion makes the acquisition task more difficult. Quondam works are proposed to capture emotion through unimodal mechanism such as only facial expressions or only vocal input. More recently, inception to the idea of multimodal emotion recognition has increased the accuracy rate of the detection of the machine. Moreover, deep learning technique with neural network extended the success ratio of machine in respect of emotion recognition. Recent works with deep learning technique has been performed with different kinds of input of human behavior such as audio-visual inputs, facial expressions, body gestures, EEG signal and related brainwaves. Still many aspects in this area to work on to improve and make a robust system will detect and classify emotions more accurately. In this paper, we tried to explore the relevant significant works, their techniques, and the effectiveness of the methods and the scope of the improvement of the results.

References

17 Cites in Article

Reference Format

Gil Levi,Tal Hassner (2014). Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns.
Dong Kunhan,Ivan Yu,Tashev (2014). Speech Emotion Recognition Using Deep Neural Network and Extreme Learning Machine.
Shiqing Zhang,Xiaohu Wang,Gang Zhang,Xiaoming Zhao (2014). Multimodal Emotion Recognition Integrating Affective Speech with Facial Expression.
N Morgan (2012). Deep and wide: Multiple layers in automatic speech recognition,‖ Audio, Speech, and Language Processing.
Abdel-Rahman Mohamed,George Dahl,Geoffrey Hinton (2012). Acoustic Modeling Using Deep Belief Networks.
G Sivaram,H Hermansky (2012). Sparse Multilayer Perceptron for Phoneme Recognition.
Martin Wöllmer,Angeliki Metallinou,Florian Eyben,Björn Schuller,Shrikanth Narayanan (2010). Context-sensitive multimodal emotion recognition from speech and facial expression using bidirectional LSTM modeling.
J Ngiam,A Khosla,M Kim,J Nam,H Lee,A Ng (2011). Multimodal deep learning.
R Brueckner,Schuller (2012). Likability classification -a not so deep neural network approach.
Björn Schuller,Stefan Steidl,Anton Batliner,Elmar Nöth,Alessandro Vinciarelli,Felix Burkhardt,Rob Son,Felix Weninger,Florian Eyben,Tobias Bocklet,Gelareh Mohammadi,Benjamin Weiss (2012). The INTERSPEECH 2012 speaker trait challenge.
Andre Stuhlsatz,Christine Meyer,Florian Eyben,Thomas Zielke,Gunter Meier,Bjorn Schuller (2011). Deep neural networks for acoustic emotion recognition: Raising the benchmarks.
Yelin Kim,Honglak Lee (2013). Deep learning for robust feature generation inaudiovisual emotion recognition.
Samira Ebrahimi,Vincent Michalski,Kishore Konda,Goethe Roland Memisevic (2015). Christopher Pal-Recurrent Neural Networks for Emotion Recognition in Video‖.
Anbang Yao,Dongqi Cai,Ping Hu,Shandong Wang,Liang Sha,Yurong Chen (2016). HoloNet: towards robust emotion recognition in the wild.
Yelin Kim,Emily Mower,Provos (2016). Data driven framework to explore patterns (timings and durations) of emotion evidence, specific to individual emotion classes.
Yin Fan,Xiangju Lu,Dian Li,Yuanliu Liu (2016). Video-based emotion recognition using CNN-RNN and C3D hybrid networks.
Zi-Jia Liu,Ya-Nan Lyu,Dong-Li Li,Hao Zhou,Yuan-Yuan Gong (2016). Intravitreal low-dose triamcinolone acetonide for nonarteritic anterior ischemic optic neuropathy.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Reeshad Khan, Omar Sharif. 2017. "A Literature Review on Emotion Recognition Using Various Methods". Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 17 (GJCST Volume 17 Issue F1).

More Citation Formats

Select Citation Style:

Download Citation

GJCST Volume 17 Issue F1

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST-F Classification I.4.8

I.7.5

Submission ReceivedDecember 9, 2016
Peer Review Double Blind
Handling Editor
Accepted January 5, 2017
Published January 15, 2017

Version of record

v1.2

Issue date

April 4, 2017

Language

English

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 142

Country: Bangladesh

Subject: Global Journal of Computer Science and Technology

Authors: Reeshad Khan, Omar Sharif (PhD/Dr. count: 0)

View Count (all-time): 403

Total Views (Real + Logic): 1958

Total Downloads (simulated): 145

Publish Date: 2017 01, Sun

Monthly Totals (Real + Logic):

Month 1: 45 views
Month 2: 58 views
Month 3: 62 views
Month 4: 55 views
Month 5: 54 views
Month 6: 37 views
Month 7: 29 views
Month 8: 45 views
Month 9: 49 views
Month 10: 31 views
Month 11: 31 views
Month 12: 37 views
Month 13: 41 views
Month 14: 25 views
Month 15: 35 views
Month 16: 15 views
Month 17: 33 views
Month 18: 24 views
Month 19: 38 views
Month 20: 31 views
Month 21: 23 views
Month 22: 25 views
Month 23: 27 views
Month 24: 32 views
Month 25: 41 views
Month 26: 34 views
Month 27: 36 views
Month 28: 28 views
Month 29: 19 views
Month 30: 35 views
Month 31: 42 views
Month 32: 25 views
Month 33: 35 views
Month 34: 20 views
Month 35: 39 views
Month 36: 25 views
Month 37: 16 views
Month 38: 40 views
Month 39: 34 views
Month 40: 40 views
Month 41: 34 views
Month 42: 23 views
Month 43: 21 views
Month 44: 24 views
Month 45: 28 views
Month 46: 17 views
Month 47: 17 views
Month 48: 34 views
Month 49: 28 views
Month 50: 26 views
Month 51: 11 views
Month 52: 40 views
Month 53: 22 views
Month 54: 48 views
Month 55: 42 views
Month 56: 30 views
Month 57: 42 views
Month 58: 31 views
Month 59: 39 views
Month 60: 11 views
Month 61: 18 views
Month 62: 30 views
Month 63: 34 views
Month 64: 39 views
Month 65: 28 views
Month 66: 19 views
Month 67: 24 views
Month 68: 20 views
Month 69: 39 views
Month 70: 31 views
Month 71: 39 views
Month 72: 33 views
Month 73: 27 views
Month 74: 31 views
Month 75: 36 views
Month 76: 23 views
Month 77: 28 views
Month 78: 13 views
Month 79: 25 views
Month 80: 32 views
Month 81: 38 views
Month 82: 25 views
Month 83: 27 views
Month 84: 19 views
Month 85: 14 views
Month 86: 26 views
Month 87: 16 views
Month 88: 32 views
Month 89: 37 views
Month 90: 18 views
Month 91: 23 views
Month 92: 35 views
Month 93: 21 views
Month 94: 26 views
Month 95: 29 views
Month 96: 26 views
Month 97: 36 views
Month 98: 37 views
Month 99: 16 views
Month 100: 21 views
Month 101: 44 views
Month 102: 28 views
Month 103: 23 views
Month 104: 24 views
Month 105: 17 views
Month 106: 33 views
Month 107: 39 views
Month 108: 69 views
Month 109: 79 views
Month 110: 18 views
Month 111: 37 views
Month 112: 33 views
Month 113: 38 views
Month 114: 45 views
Month 115: 44 views
Month 116: 55 views
Month 117: 38 views

Total Views: 2K

Total Downloads: 145

All Trends