Melola Discovery

← Melola Discovery

Jurnal SINTA 2

Emotion Recognition from Facial Expressions using Images with Pose, Illumination and Age Variation for Human-Computer/Robot Interaction

Journal of ICT Research and Applications (JICTRA) · Vol. 12 No. 1 (2018) · pp. 14–34

DOI
10.5614/itbj.ict.res.appl.2018.12.1.2
ISSN
2338-5499, 2337-5787
Published
2018-04-30
Publisher
DRI - ITB
Language
eng

Abstract

A technique for emotion recognition from facial expressions in images with simultaneous pose, illumination and age variation in real time is proposed in this paper. The basic emotions considered are anger, disgust, happy, surprise, and neutral. Feature vectors that were formed from images from the CMU-MultiPIE database for pose and illumination were used for training the classifier. For real-time implementation, Raspberry Pi II was used, which can be placed on a robot to recognize emotions in interactive real-time applications. The proposed method includes face detection using Viola Jones Haar cascade, Active Shape Model (ASM) for feature extraction, and AdaBoost for classification in real- time. Performance of the proposed method was validated in real time by testing with subjects from different age groups expressing basic emotions with varying pose and illumination. 96% recognition accuracy at an average time of 120 ms was obtained. The results are encouraging, as the proposed method gives better accuracy with higher speed compared to existing methods from the literature. The major contribution and strength of the proposed method lie in marking suitable feature points on the face, its speed and invariance to pose, illumination and age in real time.

Keywords

Collaboration network of the authors

3 authors · 2 co-authorship links · 1 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • PES University
  • Amrita Vishwa Vidyapeetham
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Sudarshan SivakumarPES University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Emotion Recognition from Facial Expressions using Images with Pose, Illumination and Age Variation for Human-Computer/Robot Interaction</dc:title>
	<dc:creator>Palaniswamy, Suja</dc:creator>
	<dc:creator>Tripathi, Shikha</dc:creator>
	<dc:subject xml:lang="en-US">classification</dc:subject>
	<dc:subject xml:lang="en-US">emotion</dc:subject>
	<dc:subject xml:lang="en-US">facial expression</dc:subject>
	<dc:subject xml:lang="en-US">feature extraction</dc:subject>
	<dc:subject xml:lang="en-US">illumination</dc:subject>
	<dc:subject xml:lang="en-US">pose</dc:subject>
	<dc:subject xml:lang="en-US">Raspberry Pi II.</dc:subject>
	<dc:description xml:lang="en-US">A technique for emotion recognition from facial expressions in images with simultaneous pose, illumination and age variation in real time is proposed in this paper. The basic emotions considered are anger, disgust, happy, surprise, and neutral. Feature vectors that were formed from images from the CMU-MultiPIE database for pose and illumination were used for training the classifier. For real-time implementation, Raspberry Pi II was used, which can be placed on a robot to recognize emotions in interactive real-time applications. The proposed method includes face detection using Viola Jones Haar cascade, Active Shape Model (ASM) for feature extraction, and AdaBoost for classification in real- time. Performance of the proposed method was validated in real time by testing with subjects from different age groups expressing basic emotions with varying pose and illumination. 96% recognition accuracy at an average time of 120 ms was obtained. The results are encouraging, as the proposed method gives better accuracy with higher speed compared to existing methods from the literature. The major contribution and strength of the proposed method lie in marking suitable feature points on the face, its speed and invariance to pose, illumination and age in real time.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2018-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/3479</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2018.12.1.2</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 12 No. 1 (2018); 14-34</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2018.12.1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/3479/3251</dc:relation>
</oai_dc:dc>