A Comparative Study of Feature Extraction Using PCA and LDA for Face Recognition
Feature extraction is important in face recognition. This paper presents a comparative study of feature extraction using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for face recognition. The evaluation parameters for the study are time and accuracy of each method....
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/244/ http://eprints.utem.edu.my/id/eprint/244/1/P140.pdf |
| Summary: | Feature extraction is important in face
recognition. This paper presents a comparative study of
feature extraction using Principal Component Analysis
(PCA) and Linear Discriminant Analysis (LDA) for face
recognition. The evaluation parameters for the study are
time and accuracy of each method. The experiments
were conducted using six datasets of face images with
different disturbance. The results showed that LDA is
much better than PCA in overall image with various
disturbances. While in time taken evaluation, PCA is
faster than LDA. |
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