This paper develops a novel framework that is capable of dealing with small sample size problem posed to subspace analysis methods for face representation and recognition. In the proposed framework, three aspects are presented. The first is the proposal of an iterative sampling technique. The second
โฆ LIBER โฆ
Generalized 2D principal component analysis for face image representation and recognition
โ Scribed by Hui Kong; Lei Wang; Eam Khwang Teoh; Xuchun Li; Jian-Gang Wang; Ronda Venkateswarlu
- Publisher
- Elsevier Science
- Year
- 2005
- Tongue
- English
- Weight
- 294 KB
- Volume
- 18
- Category
- Article
- ISSN
- 0893-6080
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Principal component analysis (PCA), a common tool from multivariate statistical analysis, has been implemented into the computer display system of a MR imaging device. PCA allows the calculation of images in which the information in a defined region of interest inherent in the basic acquired images