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Feature selection is an important pre-processing step for pattern recognition. It can discard irrelevant and redundant information that may not only affect a classifier's performance, but also tell against system's efficiency. Meanwhile, feature selection can help to identify the factors that most influence the recognition accuracy. The result can provide valuable clues to understand and reason what is the underlying distinctness among human gait-patterns. In this paper, we introduce a computationally-efficient solution to the problem of human gait feature selection. We show that feature selection based on mutual information can provide a realistic solution for high-dimensional human gait data. To assess the performance of the proposed approach, experiments are carried out based on a 73-dimensional model-based gait features set and a 64 by 64 pixels model-free gait symmetry map. The experimental results confirmed the effectiveness of the method, removing about 50% of the model-based features and 95% of the symmetry map's pixels without significant accuracy loss, which outperforms correlation and ANOVA based methods.