Principal component analysis of image gradient orientations for face recognition
We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data...
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| Format: | Conference or Workshop Item |
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2011
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| Online Access: | https://eprints.nottingham.ac.uk/31408/ |
| _version_ | 1848794195080249344 |
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| author | Tzimiropoulos, Georgios Zafeiriou, Stefanos Pantic, Maja |
| author_facet | Tzimiropoulos, Georgios Zafeiriou, Stefanos Pantic, Maja |
| author_sort | Tzimiropoulos, Georgios |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data population. We show that replacing intensities with gradient orientations and the ℓ2 norm with a cosine-based distance measure offers, to some extend, a remedy to this problem. Our scheme requires the eigen-decomposition of a covariance matrix and is as computationally efficient as standard ℓ2 intensity-based PCA. We demonstrate some of its favorable properties for the application of face recognition. |
| first_indexed | 2025-11-14T19:12:20Z |
| format | Conference or Workshop Item |
| id | nottingham-31408 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:12:20Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-314082020-05-04T20:23:29Z https://eprints.nottingham.ac.uk/31408/ Principal component analysis of image gradient orientations for face recognition Tzimiropoulos, Georgios Zafeiriou, Stefanos Pantic, Maja We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data population. We show that replacing intensities with gradient orientations and the ℓ2 norm with a cosine-based distance measure offers, to some extend, a remedy to this problem. Our scheme requires the eigen-decomposition of a covariance matrix and is as computationally efficient as standard ℓ2 intensity-based PCA. We demonstrate some of its favorable properties for the application of face recognition. 2011-03 Conference or Workshop Item PeerReviewed Tzimiropoulos, Georgios, Zafeiriou, Stefanos and Pantic, Maja (2011) Principal component analysis of image gradient orientations for face recognition. In: 2011 IEEE International Conference on Automatic Face & Gesture Recognition (FG 2011), 21-25 March 2011, Santa Barbara, California, USA. Face recognition Gradient methods Principal component analysis http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5771457&punumber%3D5765597%26filter%3DAND%28p_IS_Number%3A5771322%29%26pageNumber%3D4 |
| spellingShingle | Face recognition Gradient methods Principal component analysis Tzimiropoulos, Georgios Zafeiriou, Stefanos Pantic, Maja Principal component analysis of image gradient orientations for face recognition |
| title | Principal component analysis of image gradient orientations for face recognition |
| title_full | Principal component analysis of image gradient orientations for face recognition |
| title_fullStr | Principal component analysis of image gradient orientations for face recognition |
| title_full_unstemmed | Principal component analysis of image gradient orientations for face recognition |
| title_short | Principal component analysis of image gradient orientations for face recognition |
| title_sort | principal component analysis of image gradient orientations for face recognition |
| topic | Face recognition Gradient methods Principal component analysis |
| url | https://eprints.nottingham.ac.uk/31408/ https://eprints.nottingham.ac.uk/31408/ |