Robust Analytics for Video-Based Gait Biometrics
It addresses unobtrusive biometric authentication for populations, but appears incremental as it builds on existing methods.
The thesis tackled gait biometrics by proposing methods like Pose-Based Voting for gender identification and Genetic Template Segmentation for improved recognition, reporting that all methods outperformed existing state-of-the-art with adequate results.
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.