Development of automatic facial expression recognition system using Gabor wavelets and learning vector quantization networks

Author

Narendra K. Chennamsetty

Keywords and Phrases

Gabor wavelets; Particle swarm optimization

Abstract

"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.

Department(s)

Mechanical and Aerospace Engineering

Degree Name

M.S. in Mechanical Engineering

Publisher

University of Missouri--Rolla

Publication Date

Spring 2005

Pagination

ix, 119 pages

Rights

© 2005 Narendra Kumar Chennamsetty, All rights reserved.

Document Type

Thesis - Citation

File Type

text

Language

English

Subject Headings

Facial expression -- Mathematical models
Human face recognition (Computer science)
Wavelets (Mathematics)
Image processing -- Digital techniques
Mathematical optimization

Thesis Number

T 8756

Print OCLC #

62874075

Link to Catalog Record

Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.

Recommended Citation

Chennamsetty, Narendra K., "Development of automatic facial expression recognition system using Gabor wavelets and learning vector quantization networks" (2005). Masters Theses. 3733.
https://scholarsmine.mst.edu/masters_theses/3733