期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2015
卷号：8
期号：4
页码：113-124
DOI：10.14257/ijsip.2015.8.4.10
出版社：SERSC
摘要：Individual identification at a distance using gait features has newly gained growing interest from biometrics researchers. Most of the researchers have been shown that different covariate factors can affect different parts of the human body. In this paper, we propose a new approach that minimizes these difficulties, especially for carrying objects by combining static, dynamic, and part-based features. The Gait Features of the walking sequences are extracted by selecting only four sub bands of the Discrete Wavelet Transform (DWT) of the individual images. Moreover, Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) are implemented to extract lowest and middle frequency components that are used to create robust gait feature images (RGFIs). Then we select effective parts of the body from the Robust Gait Feature Images. After that, these parts of the body are trained using Principal Component Analysis (PCA) and Multiple Discriminant Analysis (MDA) to identify individuals. Experimental result shows promising performance in comparison with other methods
关键词：Gait feature; Sub band; Wavelet transform; Covariate factor; RGFI; FRR; ; FAR
