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Existing methods for gait recognition mainly depend on the appearance of human. Their performances are greatly affected by changes of viewing angle. To achieve higher correct classification rates for cross-view gait recognition, we develop a coupled locality preserving projections (CLPP) method in this paper. It learns coupled projection matrices to project cross-view features into a unified subspace while preserving the essential manifold structure. In the projected subspace, cross-view gait features can be matched directly. By the virtue of structure information, the learnt subspace is more robust to the view change. Experiments based on CASIA and USF gait databases are conducted to verify the efficiency of our approach.

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