Comparative study using eigenface and fisherface on face recognition system
| Main Author: | Purnawan, Andri |
|---|---|
| Format: | Thesis NonPeerReviewed Book |
| Bahasa: | ind |
| Terbitan: |
Fakultas Sains & Teknologi UAI
, 2011
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| Subjects: | |
| Online Access: |
http://eprints.uai.ac.id/427/1/ http://eprints.uai.ac.id/427/ http://perpustakaan.uai.ac.id/index.php/cari/detailkoleksi/4847A40B-EA5C-4290-90BA-0AF424B5DD4C |
Daftar Isi:
- This final project describes a study of two traditional face recognition methods, the Eigenface [2] and the Fisherface method [4]. The Eigenface is the first method considered as a successful technique of face recognition. The Eigenface method uses Principal Component Analysis (PCA) to linearly project the image space to a low dimensional feature space. The Fisherface method is an enhancement of the Eigenface method that it uses Fisher`s Linear Discriminant Analysis (FLDA or LDA) for the dimensionality reduction. The LDA maximizes the ratio of between-class scatter and minimizes the ratio of within-class scatter, therefore, it works better than PCA for purpose of discrimination. The Fisherface is especially useful when facial images have large variations in pose, facial expression, illumination and also size of training database face images. In this final project, a comparison of the Eigenface and the Fisherface methods respect to facial images having large pose, variations of expression and also size of training database face images are examined.