Human Emotion Recognition from Face Images (Hardcover, 1st ed. 2020)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	Cognitive Intelligence and Robotics
Release date:	April 2020
First published:	2020
Authors:	Paramartha Dutta • Asit Barman
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	264
Edition:	1st ed. 2020
ISBN-13:	978-981-15-3882-7
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Books > Computing & IT > Social & legal aspects of computing > Human-computer interaction Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision Books > Computing & IT > Applications of computing > Image processing > General
LSN:	981-15-3882-4
Barcode:	9789811538827

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