Title :
Human Gait Recognition Based on Dynamic and Static Features Using Generalized Regression Neural Network
Author :
Rustagi, Luv ; Kumar, Lokendra ; Pillai, G.N.
Author_Institution :
Dept. of Electr. Eng., IIT Roorkee, Roorkee, India
Abstract :
Biometric recognition using the behavioral modality of gait is an emerging research area. This paper describes a method for human gait recognition using generalized regression neural networks. The feature space is composed of a combination of dynamic (time-varying) gait signals and static body-shape parameters, extracted from binary silhouettes obtained after background subtraction from human gait sequences. The inputs to the neural network are obtained by performing discrete cosine transform (DCT) on the feature space, followed by selection of transformed coefficients to construct compact vectors.
Keywords :
biometrics (access control); discrete cosine transforms; gait analysis; image recognition; image sequences; neural nets; regression analysis; behavioral modality; binary silhouettes; biometric recognition; discrete cosine transform; dynamic gait signals; feature space; generalized regression neural network; human gait recognition; human gait sequences; static body-shape parameters; Biological neural networks; Biological system modeling; Discrete cosine transforms; Face recognition; Feature extraction; Humans; Legged locomotion; Neural networks; Sequences; Videos; biometric recognition; gait; generalized regression neural networks; static and dynamic features;
Conference_Titel :
Machine Vision, 2009. ICMV '09. Second International Conference on
Conference_Location :
Dubai
Print_ISBN :
978-0-7695-3944-7
Electronic_ISBN :
978-1-4244-5645-1
DOI :
10.1109/ICMV.2009.70
