Canonical space representation for recognizing humans by gait and face

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