Gait is one of well-identified biometrics that has been broadly applied for human identification at a distance based on their motion style. However, the current gait recognition might have difficulties due to changing the viewing angles anduncertainty associated with gait signature extraction. This study deals with the design of an intelligent gait recognition system that tackles the problems mentioned above. This system is based on spatial-domain energy deviation image as a gait signature by adopting clustering technique to estimate the gait period in the gait sequence with arbitrary walking directions. To further improve the performance of the proposed system, interval type-2 fuzzy K-nearest neighbor classifier is used to diminish the effect of uncertainty formed by variations in gait signature extraction. Interval type-2 fuzzy set is involved in extending themembership values of each gait signature by using several initial K in order to handle and manage uncertainty that exists in choosing the initial value K. The proposed method realises the reduction in the dimensions of the gait feature and over-fitting. The comprehensive analyses reveal that the proposed algorithm can significantly enhance the multiple view gait recognition performance when being matched to the similar methods in the literature.
['Saad M. Darwish']
Design of adaptive biometric gait recognition algorithm with free walking directions