Palm print recognition method based on cross gradient encoding of image with stable characteristics

Abstract

The invention belongs to the technical field of identity recognition and relates to a palm print recognition method based on cross gradient encoding of an image with stable characteristics. The palm print recognition method based on cross gradient encoding of the image with the stable characteristics comprises the steps that an angular point between the index finger and the middle finger in the palm print image and an angular point between the ring finger and the little finger in the palm print image are detected respectively, rotary correction is conducted on a tangent line which is formed through the two angular points, and then the image of a region of interest in the original palm print image is defined; an energy functional model is established after an normalized palm print image is obtained through conducting grey level normalization on the image of the region of interest, the image with the stable characteristics is obtained through solving, and then cross gradient encoding is carried out on the image with the stable characteristics to obtain cross gradient encoding characteristics which can be used for palm print matching and recognition; a matching result is output automatically after matching of a palm print is achieved. The palm print recognition method based on cross gradient encoding of the image with the stable characteristics has the advantages of being simple, high in recognition accuracy, low in algorithm complexity, short in recognition time, and high in anti-interference performance.

Description

Technical field: [0001] The invention belongs to the technical field of identification, and relates to an identification method based on human biological characteristics, in particular to a palmprint identification method with cross-gradient encoding under a stable feature image. Background technique: [0002] In today's highly informationized society, identification is one of the basic methods to strengthen the security of information and systems. Traditional identification technologies, such as using keys, password locks, ID cards, etc., are inconvenient, unsafe, and dangerous. Reliable and many other shortcomings, and biometric technology is an effective way to overcome these shortcomings. People began to study and design biometric identification technology in 1960. In June 2003, the International Civil Aviation Organization of the United Nations announced its application plan for biotechnology. Biometrics, such as fingerprints, irises, and face recognition, will be added...

AI Technical Summary

Problems solved by technology

Among the above palmprint recognition methods, the method based on the main line extraction is greatly disturbed by external factors, and the foreground and background of the palmprint are not easy to distinguish, so it is difficult to accurately extract the main line; Recognition has better recognition results, but for palmprint images, it lacks the description of texture and other information; the palmprint recognition method based on encoding uses encoding to encode the characteristics of palmprint, which can obtain relatively ideal recognition results, which is relatively The classic and effective methods are the methods listed above. PalmCode and FusionCode are the most cost-effective. They can not only obtain higher recognition accuracy, but also the algorithm complexity is not high compared with subsequent algorithms. However, the above methods have some disadvantages. Defects: In the first instance, filtering is required to smooth the image before feature extraction. The purpose is to reduce noise interference and remove some pseudo-features that affect recognition, but simply using filtering to smooth the image cannot obtain a more stable palm. It is not easy to control the degree of filtering; second, most of them use Gabor transform to extract directional features. Not only is it time-consuming to filter palmprint images, but Gabor filtering is mostly a DC component, and it is difficult to describe palmprint lines. Not the best choice; the third is that the palmprint is easily affected by rotation, translation, etc. during collection, which makes the above method less error-tolerant when using Hamming distance for matching

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