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Erschienen in:
Hesitant-Intuitionistic Trapezoidal Fuzzy Prioritized Operators Based on Einstein Operations with Their Application to Multi-criteria Group Decision-Making Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Recognizing Human Emotions from Facial Images by Landmark Triangulation: A Combined Circumcenter-Incenter-Centroid Trio Feature-Based Method

verfasst von : Avishek Nandi, Paramartha Dutta, Md Nasir

Erschienen in: Algorithms in Machine Learning Paradigms

Verlag: Springer Singapore

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Abstract

Human emotion reflected in facial expression is generated by coordinated operation of muscular movement of facial tissue which associates with the emotional state of the human subject. Facial expression is one of the most significant non-articulated forms of social communication and it is highly adopted by scientific community for the purpose of automated emotion analysis. In the present scope, a triangular structure is induced with three points, viz., circumcenter, incenter, and centroid are considered as the geometric primitive for extraction of relevant features. Information extracted from such features is utilized for the purpose of discrimination of one expression from another using MultiLayer Perceptron (MLP) classifier in images containing facial expressions available in various benchmark databases. Results obtained by applying this method found to be extremely encouraging.

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Vorheriges Kapitel Tracking Changing Human Emotions from Facial Image Sequence by Landmark Triangulation: An Incircle-Circumcircle Duo Approach
Nächstes Kapitel Stable Neighbor-Node Prediction with Multivariate Analysis in Mobile Ad Hoc Network Using RNN Model
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Metadaten
Titel
Recognizing Human Emotions from Facial Images by Landmark Triangulation: A Combined Circumcenter-Incenter-Centroid Trio Feature-Based Method
verfasst von
Avishek Nandi
Paramartha Dutta
Md Nasir
Copyright-Jahr
2020
Verlag
Springer Singapore
Buch
Algorithms in Machine Learning Paradigms

Print ISBN: 978-981-15-1040-3

Electronic ISBN: 978-981-15-1041-0

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-981-15-1041-0_9

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