Method for realizing remote authentication by fusing gait flow images (GFI) and head and shoulder procrustes mean shapes (HS-PMS)

Abstract

The invention belongs to the field of pattern recognition and particularly relates to a method for realizing remote authentication by fusing gait flow images (GFI) and head and shoulder procrustes mean shapes (HS-PMS). The method comprises the following steps of: preprocessing; estimating walking directions and determining visual angles; establishing a dynamic feature classifier for gaits; establishing a static feature for the gaits; fusing similarities between the dynamic feature classifier and the static feature according to the product rule at a matching layer to obtain decision information. According to the method for realizing remote authentication by fusing the GFIs and the HS-PMSs, the visual angles are introduced to serve as the rules of the classifiers, and thus, the problem that gait recognition is greatly influenced by the visual angle is solved. An optical flow field between adjacent two profile images is calculated by utilizing a Lacus-Kanade optical flow method, so that the real-time processing capacity of an algorithm is improved. The dynamic information and the static information of the gaits are fused, so that the separability of the method is improved, and the recognition performance is improved.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and in particular relates to a long-distance identity recognition method which combines gait optical flow graph and head-shoulder average value shape. Background technique [0002] With the development of the times and society, the demand for personal identity authentication and management continues to grow. Accurate identification of a person's identity and protection of information security have become a social problem that must be solved. With its unique uniqueness, universality, stability and non-reproducibility, biometric identification technology is widely used in security, authentication and other identification fields. At present, the biometric features that are relatively mature include: fingerprints, irises, faces, DNA, veins on the back of hands, signatures, etc. However, they usually require close-range or tactile sensing, and the recognition performance of these biometric features...

AI Technical Summary

Problems solved by technology

However, the implementation of these methods is relatively complicated, and the recognition accuracy needs to be further improved.

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