Li-Hong Juanga, Ming-Ni Wub, Shin-An Linb
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 249-256, 2018, DOI:10.1080/10798587.2016.1272777
Abstract Detecting human gender from complex background, illumination variations and objects under
computer vision system is very difficult but important for an adaptive information service. In this
paper, a preliminary design and some experimental results of gender recognition will be presented
from the walking movement that utilizes the gait-energy image (GEI) with denoised energy image
(DEI) pre-processing as a machine learning support vector machine (SVM) classifier to train and extract
its characteristics. The results show that the proposed method can adopt some characteristic values
and the accuracy can reach up to 100% gender recognition rate under More >
Login
Register
