Feasibility Study of Gabor Wavelet for Detection of Facial Expression

Mahesh Kumbhar

ISBN 10: 3659459739 / ISBN 13: 9783659459733

Published by LAP LAMBERT Academic Publishing Sep 2013, 2013

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -This book summarizes the use of Gabor wavelet in the facial expression recognition system. The recognition of facial expressions implies finding solutions to three distinct types of problems. The first one relates to detection of faces in the image. Once the face location is known, the second problem is the detection of the salient features within the facial areas. The final analysis consists by using any classification model and the extracted facial features will identify the correct facial expression. Gabor filter are used to extract the different features. The unique features for four classes (Ex. Happy, Sad, Surprise and Disgust) will be kept as a reference to the new input image. The new image features will apply to feed-forward neural networks (classifier) for recognition of four different facial expressions. In this study the Japanese Female Facial Expression (JAFFE) database used which contains expressers that expressed expressions. 88 pp. Englisch.

Seller Inventory # 9783659459733

Bibliographic Details

• Title: Feasibility Study of Gabor Wavelet for Detection of Facial Expression
• Publisher: LAP LAMBERT Academic Publishing Sep 2013
• Publication Date: 2013
• Language: English
• ISBN 10: 3659459739
• ISBN 13: 9783659459733
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

This book summarizes the use of Gabor wavelet in the facial expression recognition system. The recognition of facial expressions implies finding solutions to three distinct types of problems. The first one relates to detection of faces in the image. Once the face location is known, the second problem is the detection of the salient features within the facial areas. The final analysis consists by using any classification model and the extracted facial features will identify the correct facial expression. Gabor filter are used to extract the different features. The unique features for four classes (Ex. Happy, Sad, Surprise and Disgust) will be kept as a reference to the new input image. The new image features will apply to feed-forward neural networks (classifier) for recognition of four different facial expressions. In this study the Japanese Female Facial Expression (JAFFE) database used which contains expressers that expressed expressions.

About the Author

Qualification: Master In Engg.(Electronics & Telecommunication)HOD, Department of Electronics &Telecommunication, SVERI’s COE (Poly),Pandharpur.