Enhanced Face Recognition based on PCA and SVM

Feature Extraction and classification are important aspects of pattern recognition, computer vision. Principal Component Analysis is a well-known feature extraction and data representation technique. But this method is affected by illumination conditions. The combination o PCA an SVM for face recogn...

Full description

Saved in:
Bibliographic Details
Published in:International journal of computer applications 2015-01, Vol.117 (2), p.40-42
Main Authors: Narayana, K Venkata, R Manoj, V V, Swathi, K
Format: Article
Language:English
Subjects:
Classification
Face recognition
Feature extraction
Illumination
Pattern recognition
Preprocessing
Principal component analysis
Support vector machines
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Feature Extraction and classification are important aspects of pattern recognition, computer vision. Principal Component Analysis is a well-known feature extraction and data representation technique. But this method is affected by illumination conditions. The combination o PCA an SVM for face recognition is presented in this paper. Before applying Principal Component Analysis preprocessing o images done by using wavelet transform. After PCA is applied or feature extraction. Support Vector Machine is used or classification. Experiments based on using Indian face database. The new technique achieves better performance than using PCA only.
ISSN:0975-8887
0975-8887
DOI:10.5120/20530-2871