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 modelsHuman face recognition (Computer science)Wavelets (Mathematics)Image processing -- Digital techniquesMathematical optimization

Thesis Number

T 8756

Print OCLC #

62874075

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