Over fitting is a common problem for gait recognition algorithms when gait sequences in gallery for training are acquired under a single walking condition. In this paper, we propose an approach based on the random subspace method (RSM) to address such over learning problems. Initially, two-dimensional Principle Component Analysis (2DPCA) is adopted to obtain the full hypothesis space (i.e., eigen space). Multiple inductive biases (i.e., subspaces) are constructed, each with the corresponding basis vectors randomly chosen from the initial eigen space. This procedure can not only largely avoid over adaptation but also facilitate dimension reduction. The final classification is achieved by the decision committee which follows a majority voting criterion from the labeling results of all the subspaces. Experimental results on the benchmark USF Human ID gait database show that the proposed method is a feasible framework for gait recognition under unknown walking conditions.
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