Boulgouris, N.V., Hatzinakos, D. and Plataniotis, K.N. (2005) Gait Recognition: A Challenging Signal Processing technology for Biometric Identification. IEEE Signal Processing Magazine, 22, 78-90.
http://dx.doi.org/10.1109/MSP.2005.1550191

has been cited by the following article:

• TITLE: Gait Based Human Recognition with Various Classifiers Using Exhaustive Angle Calculations in Model Free Approach

  AUTHORS: S. M. H. Sithi Shameem Fathima, R. S. D. Wahida Banu, S. Mohamed Mansoor Roomi

  KEYWORDS: Gait Recognition, CASIA Gait Dataset B, Classifiers

  JOURNAL NAME: Circuits and Systems, Vol.7 No.8, June 14, 2016

  ABSTRACT: Human Gait recognition is emerging as a supportive biometric technique in recent years that identifies the people through the way they walk. The gait recognition in model free approaches faces the challenges like speed variation, cloth variation, illumination changes and view angle variations which result in the reduced recognition rate. The proposed algorithm selected the exhaustive angles from head to toe of a person, and also height and width of the same subject. Theexperiments were conducted using silhouettes with view angle variation, and cloth variation. Therecognition rate is improved to the extent of 91% using Support vector machine classifier. The proposed method is evaluated using CASIA Gait Dataset B (The institute of Automation, ChineseAcademy of Sciences), China. Experimental results demonstrate that the proposed technique shows promising results using state of the art classifiers.