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A nonlinear projection based human emotion recognition approach employing face point data

A nonlinear projection based human emotion recognition approach employing face point data
A nonlinear projection based human emotion recognition approach employing face point data

In human-machine interaction (HMI), human emotion recognition plays a critical role. Facial expression could be used as a means to capture human emotions. A facial expression is a nonverbal communication that can take the shape of facial muscle postures, revealing an individual's emotional conditions. In this work, a hybrid feature extraction methodology, comprising a particle swarm optimization (PSO) guided polynomial space projection and an uniform manifold approximation and projection (UMAP) technique, has been applied to the in-house Austro-Mongoloid sub-race based facial information dataset. Further, to evaluate the performance of the proposed method random forest classifier has been utilized to classify facial emotions like happiness, sadness, surprise, fear, and anger. After successful implementation of the proposed methodology a blind test accuracy of 98% has been achieved that outperforms the other competitive methods.

data projection, feature reduction, Human emotion recognition, particle swarm optimization (PSO), uniform manifold approximation and projection (UMAP)
19-24
IEEE
Bairagi, Arka
0c7cc233-a834-48e6-9a54-43840cb30d7d
Das, Bed Prakash
59fade5c-f9c9-4eec-9b83-19a37357de06
Dey, Anirban
1a4a4e23-9aa3-4e89-b4ff-fde3f0a5d389
Sharma, Kaushik Das
1267bc46-a2e4-4cf8-848d-11a56139ab52
Bairagi, Arka
0c7cc233-a834-48e6-9a54-43840cb30d7d
Das, Bed Prakash
59fade5c-f9c9-4eec-9b83-19a37357de06
Dey, Anirban
1a4a4e23-9aa3-4e89-b4ff-fde3f0a5d389
Sharma, Kaushik Das
1267bc46-a2e4-4cf8-848d-11a56139ab52

Bairagi, Arka, Das, Bed Prakash, Dey, Anirban and Sharma, Kaushik Das (2024) A nonlinear projection based human emotion recognition approach employing face point data. In 2024 IEEE 3rd International Conference on Control, Instrumentation, Energy and Communication, CIEC 2024 - Proceedings. IEEE. pp. 19-24 . (doi:10.1109/CIEC59440.2024.10468402).

Record type: Conference or Workshop Item (Paper)

Abstract

In human-machine interaction (HMI), human emotion recognition plays a critical role. Facial expression could be used as a means to capture human emotions. A facial expression is a nonverbal communication that can take the shape of facial muscle postures, revealing an individual's emotional conditions. In this work, a hybrid feature extraction methodology, comprising a particle swarm optimization (PSO) guided polynomial space projection and an uniform manifold approximation and projection (UMAP) technique, has been applied to the in-house Austro-Mongoloid sub-race based facial information dataset. Further, to evaluate the performance of the proposed method random forest classifier has been utilized to classify facial emotions like happiness, sadness, surprise, fear, and anger. After successful implementation of the proposed methodology a blind test accuracy of 98% has been achieved that outperforms the other competitive methods.

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More information

Published date: 20 March 2024
Venue - Dates: 3rd IEEE International Conference on Control, Instrumentation, Energy and Communication, CIEC 2024, , Kolkata, India, 2024-01-25 - 2024-01-27
Keywords: data projection, feature reduction, Human emotion recognition, particle swarm optimization (PSO), uniform manifold approximation and projection (UMAP)

Identifiers

Local EPrints ID: 506929
URI: http://eprints.soton.ac.uk/id/eprint/506929
PURE UUID: f1682269-0e6a-4929-ad3b-9694829ed5d4
ORCID for Bed Prakash Das: ORCID iD orcid.org/0000-0002-5025-1997

Catalogue record

Date deposited: 21 Nov 2025 17:36
Last modified: 22 Nov 2025 03:15

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Contributors

Author: Arka Bairagi
Author: Bed Prakash Das ORCID iD
Author: Anirban Dey
Author: Kaushik Das Sharma

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