Analysis of Human Gait for Designing a Recognition and Classification System
Identification and recognition of a human subject by monitoring a video/image by using various biometric features such as fingerprints, retina/iris scans, palm prints have been of interest to researches. In this chapter, an attempt has been made to recognize a human subject uniquely by monitoring his/her gait. This has been done by analyzing sampled frames of a video sequence to first detect the presence of a human form and then extract the silhouette of the subject in question. The extracted silhouette is then used to find the skeleton from it. The skeleton contains a set of points that retains the connectivity of the form and maintains the geometric properties of the silhouette. From the skeleton, a novel method has been proposed involving the neighborhood of interest pixels to identify the end points representing the heel, toe, etc. These points finally lead to the calculation of gait attributes. The extracted attributes represented in the form of a pattern vector are matched using cosine distance with features stored in the database resulting in identification/rejection.