Human Emotion Recognition from Face Images

1. Auflage 2020
ISBN: 978-981-15-3885-8
Verlag: Springer Nature Singapore

Buch, Englisch, 264 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 435 g

Reihe: Cognitive Intelligence and Robotics

This book discusses human emotion recognition from face images using different modalities, highlighting key topics in facial expression recognition, such as the grid formation, distance signature, shape signature, texture signature, feature selection, classifier design, and the combination of signatures to improve emotion recognition. 

The book explains how six basic human emotions can be recognized in various face images of the same person, as well as those available from benchmark face image databases like CK+, JAFFE, MMI, and MUG. The authors present the concept of signatures for different characteristics such as distance and shape texture, and describe the use of associated stability indices as features, supplementing the feature set with statistical parameters such as range, skewedness, kurtosis, and entropy. In addition, they demonstrate that experiments with such feature choices offer impressive results, and that performance can be further improved by combining the signatures rather than using them individually.

There is an increasing demand for emotion recognition in diverse fields, including psychotherapy, biomedicine, and security in government, public and private agencies. This book offers a valuable resource for researchers working in these areas.

Zielgruppe

Research

Autoren/Hrsg.

Barman, Asit

Dutta, Paramartha

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion

Inhaltsverzeichnis

1 Introduction1.1 About Emotion1.2 Types of Emotions1.2.1 Explanation With example1.3 Survey Work2 Human emotion recognition using Distance Signature feature2.1 Introduction2.2 Block diagram of Distance Signature2.2.1 Landmark Detection2.3 Grid Formation2.4 Feature Extraction2.5 Classification of Emotions2.6 Experiment and Result analysis3 Human emotion recognition using Shape Signature feature3.1 Introduction3.2 Block diagram of Shape Signature3.2.1 Landmark Detection3.3 Shape Formation3.4 Feature Extraction3.5 Classification of Emotions3.6 Experiment and Result analysis4 Human emotion recognition using Texture Signature feature4.1 Introduction4.2 Block diagram of Texture Signature4.2.1 Landmark Detection4.3 Texture Region Extraction4.4 Feature Extraction4.5 Classification of Emotions4.6 Experiment and Result analysis
5 Combination of Distance-Shape Signature based Emotion Detection5.1 Introduction5.2 Schematic Diagram of Facial Expression Recognition5.3 Landmark Detection using AAM5.3.1 Grid Formation5.3.2 Feature Extraction5.3.3 Combination of Distance-Shape (D-S) Signature5.4 Feature Classification by MLP Training5.5 Experiment on four benchmark databases and Result analysis5.5.1 Experiment and Result on CK+ Database5.5.2 Experiment and Result on JAFFE Database5.5.3 Experiment and Result on MMI Database5.5.4 Experiment and Result on MUG Database5.5.5 Experiment and Result of D-S Signature on CK+, JAFFE, MMI and MUG Databases5.5.6 Comparison with different state-of-the-arts5.6 ConclusionReferences6 Human emotion recognition using Texture Signature feature6.1 Introduction6.2 Block diagram of Distance-Texture Signature6.3 Landmark Detection6.4 Grid Formation6.5 Texture Region Extraction6.6 Feature Extraction6.7 Classification of Emotions6.8 Experiment and Result analysis7 Human emotion recognition using combination of Shape-Texture Signature feature7.1 Introduction7.2 Block diagram of Shape-Texture Signature7.2.1 Landmark Detection7.3 Texture Region Extraction7.4 Feature Extraction7.5 Classification of Emotions7.6 Experiment and Result analysis

Über Autor(innen)

Paramartha Dutta is currently a Professor at the Dept. of Computer and System Sciences at Visva-Bharati University, West Bengal. He completed his Bachelor's and Master's in Statistics at the Indian Statistical Institute, Kolkata, in 1988 and 1990 respectively. He received his Master's in Computer Science from the Indian Statistical Institute, Kolkata, in 1993, and his Ph.D. from the Bengal Engineering and Science University, Shibpur, in 2005.

He is the co-author of 6 books, co-editor of 10 books, and he has published over 230 research papers in peer-reviewed journals and conference proceedings. He is a Fellow of IETE, OSI, IE India, a senior member of ACM, IEEE, CSI and IACSIT, and a member of ACCS, IAPR, ISCA, ISTE, and SSI.

Asit Barman is an Assistant Professor at the Dept. of Computer Science & Engineering and Information Technology of Siliguri Institute Technology. He served as a Lecturer at the Dept. of Information Technology at the Calcutta Institute of Technology, West Bengal from 2008 to 2009.

He received his B.Tech. in Information Technology from the West Bengal University of Technology in 2006 and his M.Tech. in Information Technology from the University of  Calcutta, Kolkata, in 2008. Currently, he is pursuing a Ph.D. at the University of  Calcutta under the guidance of Prof. Paramartha Dutta. He is a member of the IEEE society.