Method for gait recognition based on visible light, infrared radiation and structured light

1:

• A method for gait recognition based on visible light, infrared radiation and structured light, comprising the following steps:
• step 1:
• operating a visible light sensor, an infrared sensor, and a structured light sensor to detect a gait of a subject so as to respectively obtain three types of raw data of the gait of the subject and preprocessing the three types of raw data to respectively obtain three types of image data with a consistent spatial mapping relationship, wherein the detection of the gait of the subject is performed with visible light, infrared radiation, and structured light that are different from each other, and the three types of image data comprise visible light data, infrared data and structured light data;
• step 2:
• encoding the visible light data into Y, U, and V channels based on a YUV encoding space, encoding the infrared data into a T channel, and encoding the structured light data into a depth channel;
• step 3:
• using two-dimensional Laplace transform to solve isotropic second-order derivatives of eight adjacent pixels in front, back, left and right directions of each pixel in the depth channel, and adding the second-order derivatives to obtain a new value of the pixel;
• step 4:
• for the depth channel processed in step 3, performing convolution operations by using two convolution operators, to determine a gradient vector, a gradient strength, and a gradient direction of each pixel;
• step 5:
• using the gradient vector to generate two mutually inverse feature convolution kernels, and using the two feature convolution kernels as weights to perform convolution operations on 3×3 regions of pixels at a corresponding pixel position in the four channels of Y, U, V, and T, so as to obtain eight feature weight maps;
• step 6:
• calculating a similarity between eight values of a same pixel position in the eight feature weight maps;
• step 7:
• setting similarity thresholds, and obtaining a corresponding fused image based on a similarity degree of each pixel;
• and
• step 8:
• extracting human head information and human skeleton information of the subject in the fused image, extracting a gait feature based on the human skeleton information, extracting a gait feature based on a preprocessed YUV visible light flow, and combining the two gait features for gait recognition;
• wherein the new value of the pixel in step 3 is obtained according to the following formula:
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• ∇2ƒ(x, y) represents second-order partial derivative processing performed on a function ƒ(x, y);
• (x, y) represents coordinates of the pixel, x is the abscissa, and y is the ordinate;
• ∂ represents a partial derivative symbol;
• ƒ represents ƒ(x, y);
• and υ1, υ2, υ3, and υ4 represent unit vectors of four directions of 0°, 90°, 180°, and 270° respectively.