Abstract
Human gait is a spatio-temporal phenomenon and typifies the motion characteristics of an individual. The gait of a person is easily recognisable when extracted from a side view of the person. Accordingly, gait-recognition algorithms work best when presented with images where the person walks parallel to the camera (i.e. the image plane). A set of stances or key frames that occur during the walk cycle of an individual is chosen. This paper presents a novel approach adopted in automatic gait recognition in which the silhouette extracted is represented using Shannon entropy and extracts the height of the subject and periodicity of the gait. To classify unknown gait, they need to match the nearest neighbour in the stored database of extracted gait features, and the proposed approach are tested on the data sets and is found to be quite satisfactory in natural walk conditions. In addition, the proposed decision fusion enables the performance improvement by integrating multiple ones with different confidence measures.
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