Subspace-based Super-resolution for Face Recognition from Video
| Autor: | Osman Gökhan Sezer, Yucel Altunbasak, Aytül Erçil |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Clustering high-dimensional data
business.industry Computer science Feature extraction Pattern recognition Facial recognition system Computer Science::Computer Vision and Pattern Recognition Motion estimation Face (geometry) Feature (machine learning) Three-dimensional face recognition Computer vision Artificial intelligence Noise (video) business Image resolution Subspace topology |
| Zdroj: | 2006 IEEE 14th Signal Processing and Communications Applications. |
| DOI: | 10.1109/siu.2006.1659905 |
| Popis: | Performance of current face recognition algorithms reduces significantly when they are applied to low-resolution face images. To handle this problem, superresolution techniques can be applied either in the pixel domain or in the face subspace. Since face images are high dimensional data which are mostly redundant for the face recognition task, feature extraction methods that reduce the dimension of the data are becoming standard for face analysis. Hence, applying super-resolution in this feature domain, in other words in face subspace, rather than in pixel domain, brings many advantages in computation together with robustness against noise and motion estimation errors. Therefore, we propose new super-resolution algorithms using Bayesian estimation and projection onto convex sets methods in feature domain and present a comparative analysis of the proposed algorithms and those already in the literature. |
| Databáze: | OpenAIRE |
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