Gait recognition using occluded data

Author

Isa, Wan Noorshahida Mohd ; Alam, Md Jahangir ; Eswaran, Chikkanan

Author_Institution

Fac. of Inf. Technol., Multimedia Univ., Cyberjaya, Malaysia

fYear

2010

fDate

6-9 Dec. 2010

Firstpage

344

Lastpage

347

Abstract

Gait is an attractive biometrics for use in monitoring and surveillance applications. In such settings, occlusion is common and may affect recognition. This paper investigates the performance of gait using occluded data. To reconstruct the data, interpolation is applied to the occluded data using the Support Vector Machines for Regression (SVR) framework. Then the Principal Component Analysis (PCA) and Canonical Analysis (CA) are applied to reduce the dimensionality of the reconstructed data and classification. Comparison is made between the recognition accuracy rates obtained using the occluded and visible data of the same subject.

Keywords

biometrics (access control); computer graphics; computer vision; gait analysis; image recognition; principal component analysis; support vector machines; canonical analysis; data reconstruction; gait recognition; interpolation; occluded data; principal component analysis; regression framework; support vector machines; vision-based systems; Hip; Interpolation; Kernel; Knee; Leg; Principal component analysis; Support vector machines; Canonical Analysis; Gait Occlusion; Gait as Biometrics; Principal Component Analysis; Support Vector Machines for Regression;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems (APCCAS), 2010 IEEE Asia Pacific Conference on

Conference_Location

Kuala Lumpur

Print_ISBN

978-1-4244-7454-7

Type

conf

DOI

10.1109/APCCAS.2010.5774992

Filename

5774992