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Novel averaging window filter for SIFT in infrared face recognition 被引量：1

Novel averaging window filter for SIFT in infrared face recognition
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摘要 The extraction of stable local features directly affects the performance of infrared face recognition al- gorithms. Recent studies on the application of scale invariant feature transform （SIFT） to infrared face recognition show that star-styled window filter （SWF） can filter out errors incorrectly introduced by SIFT. The current letter proposes an improved filter pattern called Y-styled window filter （YWF） to further elim- inate the wrong matches. Compared with SWF, YWF patterns are sparser and do not maintain rotation invariance; thus, they are more suitable to infrared face recognition. Our experimental results demonstrate that a YWF-based averaging window outperforms an SWF-based one in reducing wrong matches, therefore The extraction of stable local features directly affects the performance of infrared face recognition al- gorithms. Recent studies on the application of scale invariant feature transform （SIFT） to infrared face recognition show that star-styled window filter （SWF） can filter out errors incorrectly introduced by SIFT. The current letter proposes an improved filter pattern called Y-styled window filter （YWF） to further elim- inate the wrong matches. Compared with SWF, YWF patterns are sparser and do not maintain rotation invariance; thus, they are more suitable to infrared face recognition. Our experimental results demonstrate that a YWF-based averaging window outperforms an SWF-based one in reducing wrong matches, therefore
作者 柏俊峰 马泳 黎静 樊凡 王宏远
机构地区 Department of Electronic and Information Engineering Department of Electronic and Information Engineering
出处 《Chinese Optics Letters》 SCIE EI CAS CSCD 2011年第8期20-23,共4页 中国光学快报（英文版）
基金 supported by the Natural Science Foundation of Hubei Province under Grant No. 2009CDB320
关键词 Feature extraction Feature extraction
分类号 TN219 [电子电信—物理电子学] TP391.41 [自动化与计算机技术—计算机应用技术]
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同被引文献23

  • 1查宇飞,毕笃彦.基于小波变换的自适应多阈值图像去噪[J].中国图象图形学报（A辑）,2005,10(5):567-570. 被引量：50
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  • 4LOWE D G. Distinctive image features from scale invari- ant key points [ J ]. International Journal of Computer Vi- sion,2004,60 (2) :91-110.
  • 5YAN K, SUKTHANKAR R. PCA-SIb~F:A more distinctive representation for local image descriptors[ C ]. IEEE Com- puter Society Conference on Computer Vision and Pattern Recognition, Washington, USA, 2004 (2): 11/506- I1/513.
  • 6ABDEL-HAKIM A E, FARAG A A. CSIFT: A sift de- scriptor with color invariant characteristics [ C ]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, USA ,2006 : 1978-1983.
  • 7HERBERT B,ANDREAS E,TUYTELAARS T,et al. SURF: Speeded-Up Robust Features [ J ]. Computer Vision and Im- age Understanding ,2008,110(3) :346-359.
  • 8RACHEL L T, SIEBERT J P. Local feature extraction and matching on range images:2.5D SIFT[ J]. Computer Vi- sion and Image Understanding, 2009, 113 (12): 1235-1250.
  • 9CHEUNG W, HAMARNEH G. n-SIFT: n-Dimensional scale invariant feature transform [ J]. IEEE Transactions on Image Processintang ,2009,18 ( 9 ) :2012-2021.
  • 10MIKOLAJCZYK K,SCHMID C. A performance evaluation of local descriptors [ C ]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005 ( 2 ) : I1/257- I1/263.

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Chinese Optics Letters
2011年 第8期

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