Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. · 2004 · 64 citations · 12 references
ComputingComputer ScienceComputer VisionDigital ImagingMachine VisionDimensionality ReductionFeature ExtractionImage AnalysisImage ProcessingPattern RecognitionPrincipal Component AnalysisIris ImagingIris RecognitionPrincipal Components
It is very important for the performance evaluation of iris recognition algorithms to construct very large iris databases. However, limited by the real conditions, there are no very large common iris databases now. In this paper, an iris image synthesis method based on principal component analysis (PCA) and super-resolution is proposed. The iris recognition algorithm based on PCA is first introduced and then, iris image synthesis method is presented. The synthesis method first constructs coarse iris images with the given coefficients. Then, synthesized iris images are enhanced using super-resolution. Through controlling the coefficients, we can create many iris images with specified classes. Extensive experiments show that the synthesized iris images have satisfactory cluster and the synthesized iris databases can be very large.
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Efficient Iris Recognition by Characterizing Key Local Variations
Longyu Ma, Tieniu Tan, Yunlong Wang et al. · IEEE Transactions on Image Processing · 2004 · 927 citations