AbstractWith my thesis,I have established a novel human-identification scheme from long range face-gait profiles in surveillance videos. I investigated the role of multi view face-gait images acquired from multiple cameras,the importance of surveillance and visible range images in ascertaining identity,the impact of multimodal fusion,and efficient subspace features and classifier methods,and along with side face-ear biometric traits; the role of soft/secondary biometric (walking style) in enhancing the accuracy and robustness of the identification systems. An extensive,experimental evaluation of several subspace based side face-ear,gait feature extraction approaches and learning classifier methods on different datasets from publicly available databases (CASIA-China,Human Action Database- Sweden and UCMG Database-University of Canberra) has shown a significant improvement in recognition accuracy and robustness with multimodal fusion of multi-view face-ear,gait images from visible and infrared cameras acquired from different video surveillance scenarios.
|Date of Award||1 Jan 2014|
Investigating adaptive multi-modal approaches for person identity verification based on face and gait fusion
Hossain, S. M. E. (Author). 1 Jan 2014
Student thesis: Doctoral Thesis