  Gait analysis is the study of the systematic methods that assess and quantify
animal locomotion. Gait finds a unique importance among the many
state-of-the-art biometric systems since it does not require the subject's
cooperation to the extent required by other modalities. Hence by nature, it is
an unobtrusive biometric.
  This thesis discusses both hard and soft biometric characteristics of gait.
It shows how to identify gender based on gait alone through the Posed-Based
Voting scheme. It then describes improving gait recognition accuracy using
Genetic Template Segmentation. Members of a wide population can be
authenticated using Multiperson Signature Mapping. Finally, the mapping can be
improved in a smaller population using Bayesian Thresholding. All methods
proposed in this thesis have outperformed their existing state of the art with
adequate experimentation and results.
