Generalized N-Dimensional Principal Component Analysis (GND-PCA) Based Statistical Appearance Modeling of Facial Images with Multiple Modes
| Autor: | Yen-Wei Chen, Takanori Igarashi, Keisuke Nakao, Rui Xu, Akio Kashimoto, Xu Qiao |
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| Rok vydání: | 2009 |
| Předmět: |
N dimensional
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Multiple modes ComputingMethodologies_PATTERNRECOGNITION Reconstruction error Computer Science::Computer Vision and Pattern Recognition Principal component analysis Computer vision Computer Vision and Pattern Recognition Artificial intelligence Appearance modeling business |
| Zdroj: | IPSJ Transactions on Computer Vision and Applications. 1:231-241 |
| ISSN: | 1882-6695 |
| DOI: | 10.2197/ipsjtcva.1.231 |
| Popis: | This paper introduces a framework called generalized N-dimensional principal component analysis (GND-PCA) for statistical appearance modeling of facial images with multiple modes including different people, different viewpoint and different illumination. The facial images with multiple modes can be considered as high-dimensional data. GND-PCA can represent the high-order dimensional data more efficiently. We conduct extensive experiments on MaVIC Database (KAO-Ritsumeikan Multi-angle View, Illumination and Cosmetic Facial Database) to evaluate the effectiveness of the proposed algorithm and compared the conventional ND-PCA in terms of reconstruction error. The results indicated that the extraction of data features is computationally more efficient using GND-PCA than PCA and ND-PCA. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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