| Abstract | We propose a view-based approach to recognize humans from their gait. Two different imagefeatures have been considered: the width of the outer contour of the binarized silhouette of the
walking person and the entire binary silhouette itself. To obtain the observation vector from the image
features we employ two different methods. In the first method referred to as the indirect approach,
the high-dimensional image feature is transformed to a lower-dimensional space by generating what we call the Frame to Exemplar (FED) distance. The FED vector captures both structural and
dynamic traits of each individual. For compact and effective gait representation and recognition,
the gait information in the FED vector sequences is captured in a hidden Markov model (HMM). In
the second method referred to as the direct approach, we work with the feature vector directly (as
opposed to computing the FED) and train an HMM. We estimate the HMM parameters (specifically
the observation probability B) based on the distance between the exemplars and the image features.
In this way we avoid learning high-dimensional probability density functions. The statistical nature
of the HMM lends overall robustness to representation and recognition. The performance of the
methods is illustrated using several databases.
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