Title :
Gait recognition based on KPCA and KNN
Author :
Suo, Ning ; Qian, Xu ; Zhao, Jinhui
Author_Institution :
Sch. of Mech. Electron. & Inf. Eng., China Univ. of Min. & Technol., Beijing, China
Abstract :
This paper presents a novel approach for human identification at a distance using gait recognition. The proposed work introduces a nonlinear machine learning method, Kernel Principal Component Analysis (KPCA), and K nearest neighbor classification (KNN) classifier for gait recognition. Kernel Principal Component Analysis (KPCA) is first applied to 1-dimension signals derived from a sequence of silhouette images to reduce its dimensionality. Then, we performed K nearest neighbor classification (KNN) for gait recognition. The experimental results show the KPCA and KNN based gait recognition algorithm is better than that based on PCA.
Keywords :
image classification; image recognition; image sequences; learning (artificial intelligence); principal component analysis; K nearest neighbor classification; KNN; KPCA; PCA; gait recognition; kernel principal component analysis; nonlinear machine learning method; silhouette image sequence; Kernel; Gait recognition; KNN; KPCA; PCA;
Conference_Titel :
Environmental Science and Information Application Technology (ESIAT), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-7387-8
DOI :
10.1109/ESIAT.2010.5568300