A novel illumination normalization method based on local relation map
This paper presents a novel illumination normalization method to address the issue of illumination invariant face recognition. The proposed method applies a Difference of Gaussians (DoG) filter in the logarithm domain of the images to reduce the effects caused by the shadows. After that, a local rel...
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| Main Authors: | , , |
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| 其他作者: | |
| 格式: | Conference or Workshop Item |
| 語言: | English |
| 出版: |
2013
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/101917 http://hdl.handle.net/10220/12788 |
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| 總結: | This paper presents a novel illumination normalization method to address the issue of illumination invariant face recognition. The proposed method applies a Difference of Gaussians (DoG) filter in the logarithm domain of the images to reduce the effects caused by the shadows. After that, a local relation map (LRM) is extracted as illumination invariant features for further recognition task. The proposed method outperforms the existing normalization approaches significantly based on the experimental results in the Yale B and Extended Yale B database. Moreover, the proposed method does not involve any prior information or modeling step and takes a low computational loan. Therefore it can be easily implemented in a real-time face recognition system. |
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