Infrared gait dual-channel feature fusion recognition method

Abstract

An infrared gait dual-channel feature fusion recognition method, the steps are: infrared image preprocessing, firstly using the background subtraction method of the mixed Gaussian model to realize the detection of moving human body; then performing morphological processing, that is, using morphological filtering to eliminate binary values Noise in the image and fill in the lack of moving targets; infrared image feature extraction, first of all, it is necessary to divide the cycle of the gait sequence, and determine the gait cycle based on the periodicity of the contour width; determine the gait energy map; infrared pyroelectric features Extraction, first of all, data collection, then denoising processing on the collected voltage signal, and then performing Fourier transform on the time domain signal to obtain its spectrum, and then use PCA to reduce the dimension to obtain the feature vector; classification and identification. In the present invention, the one-dimensional voltage signal collected by infrared pyroelectricity during human movement is easy to realize in real time, and the infrared image is not sensitive to the shape change of the carrying objects such as bags, umbrellas, backpacks, etc. when people walk, and it can also be used at night working.

Description

technical field [0001] The invention relates to an infrared gait double-channel feature fusion recognition method. In particular, it relates to an infrared gait dual-channel feature fusion recognition method that combines infrared pyroelectricity and infrared imaging to identify human identity. Background technique [0002] Biometric identification technology is a new technology that uses the human body's innate physiological characteristics or inherent behavioral characteristics for identity authentication. Physiological characteristics such as fingerprints, irises, and facial images usually require close-range or contact perception, while gait is a behavioral characteristic that can be perceived in long-distance situations. Gait recognition has aroused strong interest in the field of computer vision due to its advantages of non-invasiveness, long-distance, and difficult to camouflage, and has become a frontier direction that has attracted much attention in biometric recog...

AI Technical Summary

Problems solved by technology

Lipton et al. used the difference between two consecutive frames of images to detect moving objects; Collins et al. proposed a 3-frame temporal difference method, which improved the sensitivity to noise; Chang et al. improved the traditional inter-frame difference method, combined with connected components Marking, morphological closed operation and human body structure analysis to filter out the influence of noise and shadows to extract moving targets; the method of inter-frame difference is simple, fast in operation, easy to implement, and can quickly adapt to changes in dynamic scenes; its disadvantages are Sensitive to noise, when the internal brightness of the moving object is relatively uniform, only the edge of the target can be detected, and the entire object cannot be detected

Although the model-based method can effectively improve the occlusion problem, due to the complexity of the algorithm, it conflicts with the real-time requirements.

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