IEEE Transactions on Circuits and Systems for Video Technology · 2004 · 304 citations · 32 references
ComputingComputer ScienceBiomedical ImagingComputer VisionDigital ImagingDigital Image ProcessingFeature DetectionFeature ExtractionImage AnalysisImage ProcessingMedical Image AnalysisBiometric Feature ExtractionFourier's LawFourier's Law Of Heat ConductionImage Feature ExtractionMultiresolution RepresentationMultiresolution Spatial RepresentationsThermal ImagingThermal Infrared Imaging
A novel approach to personal verification using the thermal images of palm-dorsa vein patterns is presented in this paper. The characteristics of the proposed method are that no prior knowledge about the objects is necessary and the parameters can be set automatically. In our work, an infrared (IR) camera is adopted as the input device to capture the thermal images of the palm-dorsa. In the proposed approach, two of the finger webs are automatically selected as the datum points to define the region of interest (ROI) on the thermal images. Within each ROI, feature points of the vein patterns (FPVPs) are extracted by modifying the basic tool of watershed transformation based on the properties of thermal images. According to the heat conduction law (the Fourier law), multiple features can be extracted from each FPVP for verification. Multiresolution representations of images with FPVPs are obtained using multiple multiresolution filters (MRFs) that extract the dominant points by filtering miscellaneous features for each FPVP. A hierarchical integrating function is then applied to integrate multiple features and multiresolution representations. The former is integrated by an inter-to-intra personal variation ratio and the latter is integrated by a positive Boolean function. We also introduce a logical and reasonable method to select a trained threshold for verification. Experiments were conducted using the thermal images of palm-dorsas and the results are satisfactory with an acceptable accuracy rate (FRR:2.3% and FAR:2.3%). The experimental results demonstrate that our proposed approach is valid and effective for vein-pattern verification.
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Jan J. Koenderink · Biological Cybernetics · 1984 · 2.4K citations
Image processing, analysis and machine vision
Tristan Dijkstra · Neurocomputing · 1994 · 1.8K citations
Personal authentication using palm-print features
Chin-Chuan Han, Hsu-Liang Cheng, Chih-Lung Lin et al. · Pattern Recognition · 2002 · 474 citations