A gait recognition method based on an extreme learning machine

Abstract

The invention discloses a gait recognition method based on an extreme learning machine. The method comprises the steps of preprocessing, feature extraction and classification recognition, and specifically comprises the following steps that the preprocessing is used for obtaining a standard moving target contour sequence with the uniform size, and the steps are included and comprises: 1-1, extracting a moving target contour sequence; 1-2, image standardization; The feature extraction is used for obtaining gait feature parameters with good characterization, and comprises the following steps: 2-1, gait period extraction; 2-2, extracting an action energy diagram; 2-3, dimension reduction is carried out through two-dimensional principal component analysis; Classification recognition is carriedout by adopting a kernel extreme learning machine (KELM); 3-1, constructing a kernel extreme learning machine neural network model; 3-2, training a neural network of the kernel extreme learning machine; 3-3, performing classifying and identifying. The action energy diagram extracted by the method contains more dynamic and static information, a complex image processing process is not needed, the extraction mode is simple, and the method has very good characterization.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a gait recognition method based on an extreme learning machine and an action energy graph. Background technique [0002] In recent years, the extensive research and application of biometric identification technology has effectively promoted the improvement and progress of the intelligence level of the security system. As a kind of biometrics, gait refers to the posture and way of walking. Important biometrics that can be captured via video. Compared with other biometrics, using gait as a biometric can identify people in low-resolution video images. No cooperation or physical contact of the monitored person is required. At the same time, the gait is difficult to camouflage, imitate or hide, and has non-invasive characteristics. Based on the above advantages, gait can be widely used in access control and security identification in prisons, airports, banks...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to overcome the problem that the existing gait recognition technology cannot coexist with the calculation efficiency and the recognition accuracy, realize the optimal balance between the calculation efficiency and the recognition accuracy, and propose a faster and more accurate gait based on the extreme learning machine recognition methods

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