Canonical Space Representation for Recognizing Humans by Gait and Face

Huang, P.S., Harris, C.J. and Nixon, M.S. (1998) Canonical Space Representation for Recognizing Humans by Gait and Face. Proc. of IEEE Southwest Symposium on Image Analysis and Interpretation. pp. 180-185 .

Record type: Conference or Workshop Item (Other)

Abstract

Eigenspace transformation (EST) based on Principal Component Analysis (PCA) has been demonstrated to be a potent metric in face recognition and gait analysis, but without using data analysis to increase classification capability. In this paper, we propose a new approach which combines canonical space transformation (CST) based on Canonical Analysis (CA) with the eigenspace transformation for feature extraction. This method can be used to reduce data dimensionality and to optimise the class separability of different gait sequences and face classes simultaneously. Each image template is projected from high-dimensional image space to a single point in low-dimensional canonical space. In this new space the recognition of human gait and faces becomes much simpler. Experimental results for human gait analysis and face recognition show this method is superior to use EST or CST alone. As such, the combination of PCA and CA is shown to be of considerable advantage in an emerging new biometric.

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