Abstract
The dorsal hand vein images captured by cross-device may have great differences in brightness, displacement, rotation angle and size. These deviations must influence greatly the results of dorsal hand vein recognition. To solve these problems, the method of dorsal hand vein recognition was put forward based on bit plane and block mutual information in this paper. Firstly, the input gray image of dorsal hand vein was converted to eight-bit planes to overcome the interference of brightness inside the higher bit planes and the interference of noise inside the lower bit planes. Secondly, the texture of each bit plane of dorsal hand vein was described by a block method and the mutual information between blocks was calculated as texture features by three kinds of modes to solve the problem of rotation and size. Finally, the experiments cross-device were carried out. One device was used to be registered, the other was used to recognize. Compared with the SIFT (Scale-invariant feature transform, SIFT) algorithm, the new algorithm can increase the recognition rate of dorsal hand vein from 86.60% to 93.33%.
Highlights
Biometric is a technique that uses inherent and unique biometric feature to recognize people identification [1]
The concept of bit bit planes planes is illustrated by image a abundant gray information and overcome the interference brightness and noise caused by the collection environment, we studied the bit planes generated by gray image that only retains the contour of dorsal hand vein
It can be found that the recognition rate of the gray-normalized image is less than 50%, and the binary image reaches 86.60%, while the gray image that only retains the contour of dorsal hand vein reaches 89.67%
Summary
Biometric is a technique that uses inherent and unique biometric feature to recognize people identification [1]. It hasfull had use a significant improvement (WSM) [10]neighborhood containing structure width and information It makes of the global shape in themaking recognition in single-device experiments, and a higher recognition rate than the traditional information, the rate ability to characterize vein features stronger. The optimal bit plane was selected to overcome the influence of brightness the mutual information among different blocks was calculated as texture features by three kinds of and noise. The texture features of dorsal hand vein were described by a block method, and the mutual information calculation modes. The recognition rate of dorsal hand vein images under a cross-device mutual information among different blocks was calculated as texture features by three kinds of mutual increased toinformation. The recognition rate of dorsal hand vein images under a cross-device increased to 93.33%
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