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 reature 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 e...
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| 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/15091/1/A%20comparative%20study%20of%20feature%20extraction%20using%20PCA%20and%20LDA%20for%20face%20recognition260.pdf http://eprints.utem.edu.my/id/eprint/15091/ |
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| Summary: | Feature extraction is important in face recognition. This paper presents a comparative study of reature 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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