Development of automatic facial expression recognition system using Gabor wavelets and learning vector quantization networks
Keywords and Phrases
Gabor wavelets; Particle swarm optimization
"In the present work, an automatic Facial Expression Recognition (FER) system is developed based on Gabor-wavelet methodology and learning vector quantization networks (LVQ). Facial attributes from the frontal images are extracted in the form of feature vectors by evaluating the responses from a set of 18 complex Gabor filters at 34 reference points on a face. The resultant high-dimensional feature vectors are condensed by performing Principal Component Analysis (PCA) coupled with Singular Value Decomposition (SVD) and are classified into classes of expression: anger, distress, sad, surprise, normal and happy, using LVQ networks"--Abstract, page iii.
Mechanical and Aerospace Engineering
M.S. in Mechanical Engineering
University of Missouri--Rolla
ix, 119 pages
© 2005 Narendra Kumar Chennamsetty, All rights reserved.
Thesis - Citation
Facial expression -- Mathematical models
Human face recognition (Computer science)
Image processing -- Digital techniques
Print OCLC #
Link to Catalog Record
Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.http://merlin.lib.umsystem.edu/record=b5464268~S5
Chennamsetty, Narendra K., "Development of automatic facial expression recognition system using Gabor wavelets and learning vector quantization networks" (2005). Masters Theses. 3733.
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