A new gait gender recognition method is proposed by using local entropy descriptor to extract information from gait motion history image. First

gait period detection is carried out to obtain a motion history image containing a period

and then the information entropy of neighborhood is calculated taking each pixel point as center

as the local entropy descriptor feature. Then the principal component analysis method is used to reduce the dimension. Finally

the gender classification experiment is carried out on the CASIA B gait database using this feature. Experimental results show that this method has good gender recognition effect

and the average recognition rate is better than the methods based on basic motion history image and gait energy image.