Human Emotion Recognition from Face Images (Cognitive Intelligence and Robotics) - Tapa dura

Acerca del autor

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.

De la contraportada

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.