Palmprint recognizing method and device

Abstract

The invention discloses a palmprint recognizing method and device, relating to the field of biological feature recognition. The palmprint recognizing method comprises the steps of: sequentially intercepting a plurality of local areas on an area in which a palmprint image is interested and acquiring a contrast context vector of each local area; extracting stable areas from the plurality of local areas according to the contrast context vector of each local area; carrying out feature coding on pixels in the stable areas; and carrying out similarity judgment on palmprint features according to theresult of feature coding. According to the invention, prior to feature matching, the stable areas in the area in which a palmprint image is interested are extracted by using the contrast context vectors and the influence on a recognition result when unstable areas are matched is removed; and compared with the prior art, the palmprint recognizing method and device have the advantages that: the matching accuracy is higher and the influence caused by change of palmprint images is less.

Description

technical field [0001] The invention relates to the field of biological feature identification, and more specifically, to a palmprint identification method and device. Background technique [0002] With the rapid development of information technology, information security becomes more and more important in people's life. In many occasions, such as entry-exit registration, access control of key banking departments, e-commerce, e-government, judicial security systems, get off work attendance, computer login, etc., all require accurate identification of people's identities. Among them, biometric technology has received more and more attention because of its reliability and convenience, and has been widely used in various identity authentication scenarios. At present, the main biometric features used are divided into two categories: one is physiological features, such as fingerprints, palm prints, irises, faces, etc.; the other is behavioral features, such as signatures, voices...

AI Technical Summary

Problems solved by technology

This method is susceptible to changes in the acquisition environment and palm pose

In the ROI area, some local areas have weak or no obvious lines, and their texture directions are easy to change during acquisition, and this method performs feature encoding on all pixels in the ROI area indiscriminately. , the images collected at different times will extract different encoding results, making these unstable features during recognition will lead to a decline in recognition performance

In order to ensure the recognition performance, this method often has high requirements on the collection environment and devices in the application. The palm is required to be fixed in the collection device during collection, the user experience is relatively poor, and the equipment is also relatively large.

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