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Recognizing Humans by Gait using a Statistical Approach forTemporal Templates

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In this paper, we propose a new approach which combines canonical space transformation (CST) with the eigenspace transformation (EST) for feature extraction of temporal templates in a gait sequence. Eigenspace transformation has been demonstrated to be a potent metric in automatic face recognition and gait analysis, but without using data analysis to increase classification capability. Our method can be used to reduce data dimensionality and to optimize the class separability of different gait sequences simultaneously. Each temporal 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 by template matching becomes much faster and simpler. Experimental results for human gait analysis show this method is superior to eigenspace representation. As such, the combination of EST and CST is shown to be of considerable advantage in an emerging new biometric.

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