Feasibility Study of Gabor Wavelet for Detection of Facial Expression

Mahesh Kumbhar

ISBN 10: 3659459739 / ISBN 13: 9783659459733

Editore: LAP LAMBERT Academic Publishing Sep 2013, 2013

Riguardo questo articolo

This item is printed on demand - Print on Demand Titel. 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 88 pp. Englisch.

Codice articolo 9783659459733

Dati bibliografici

• Titolo: Feasibility Study of Gabor Wavelet for Detection of Facial Expression
• Editore: LAP LAMBERT Academic Publishing Sep 2013
• Data di pubblicazione: 2013
• Lingua: Inglese
• ISBN 10: 3659459739
• ISBN 13: 9783659459733
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 220 L x 150 A x 6 P (millimetri) 220 millimetri di larghezza per 150 millimetri di altezza per 6 millimetri di profondità
• Peso: 149 grammi

Descrizione articolo

Riassunto

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.

L'autore

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