Abstract—Gait based recognition is one of the emerging new
biometric technology for human identification, surveillance and
other security applications. Gait is a potential behavioral feature
to identify humans at a distance based on their motion. The use
of new methods for handling inaccurate information about gait
features is of fundamental important. This paper deals with the
design of an intelligent gait recognition system using interval
type-2 fuzzy K-nearest neighbor (IT2FKNN) for diminishing the
effect of uncertainty formed by variations in energy deviation
image (EDI). The proposed system is built on top of the
well-known principal component analysis (PCA) method that is
utilized to remove correlation between the features and also to
reduce its dimensionality. Our system employs IT2FKNN to
compute fuzzy within and in-between class scatter matrices of
PCA to refine classification results. This employment makes the
system able to distinguish between normal, abnormal and
suspicious walk of a person so that an alarming action may be
taken well in time. Interval type-2 fuzzy set is involved to extend
the membership values of each gait signatures by using several
initial K in order to handle and manage uncertainty that exist in
choosing initial K. The result of the experiments conducted on
gait database show that the proposed gait recognition approach
can obtain encouraging accurate recognition rate.
Index Terms—Biometric, gait recognition, interval type-2 fuzzy KNN, PCA.
Saad M. Darwish and Adel A. El Zoghabi are with the Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 163 Horreya Avenue, El Shatby 21526, P.O. Box 832, Alexandria, Egypt, (tel.: +203)4295007; e-mail: Saad.email@example.com, firstname.lastname@example.org).
Oday A. Hassen is with the Department of Mathematics, College of Science, University of Mustansiriya, Iraq (e-mail: email@example.com).
Cite: Saad M. Darwish, Adel A. El-Zoghabi, and Oday A. Hassen, "A Modified Walk Recognition System for Human Identification Based on Uncertainty Eigen Gait," International Journal of Machine Learning and Computing vol.4, no. 4, pp. 346-353, 2014.