Comparing Different Template Features for Recognizing People by Their Gait | Zendy

Ping Huang | Zendy; C.J. Harris | Zendy; Mark Nixon | Zendy

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Comparing Different Template Features for Recognizing People by Their Gait

Author(s) -

Ping Huang,

C.J. Harris,

Mark Nixon

Publication year - 1998

Publication title -

eprints soton (university of southampton)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.5244/c.12.64

Subject(s) - template , computer science , pattern recognition (psychology) , artificial intelligence , gait , curse of dimensionality , transformation (genetics) , optical flow , feature extraction , feature vector , computer vision , feature (linguistics) , image (mathematics) , physiology , biochemistry , chemistry , linguistics , philosophy , gene , biology , programming language

To recognize people by their gait from a sequence of images, we have proposed a statistical approach which combined eigenspace transformation (EST) with canonical space transformation (CST) for feature transformation of spatial templates. This approach is used to reduce data dimensionality and to optimize the class separability of different gait sequences simultaneously. Good recognition rates have been achieved. Here, we incorporate temporal information from optical flows into three kinds of temporal templates and use them as features for gait recognition in addition to the spatial templates. The recognition performance for four kinds of template features has been evaluated in this paper. Experimental results show that spatial templates, horizontal-flow templates and the combined horizontal-flow and vertical-flow templates are better than vertical-flow templates for gait recognition.

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