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Recently, many gait recognition algorithms are proposed, and the optimal camera arrangement is necessary to maximize the performance. In this paper, we propose the optimal camera arrangement by using a performance model that considers observation conditions comprehensively. We select silhouette resolution, observation view, and its local and global changes as the observation conditions affecting the performance. Then, training sets composed of pairs of the observation conditions and the performance is obtained by gait recognition experiments under several camera arrangements. A performance model is constructed by applying Gaussian Processes Regression to the training set. The optimal arrangement is determined by estimating the performance for each camera arrangement with the performance model. The effectiveness of the proposed method is demonstrated by experiments of performance estimation with a training set including 17 subjects and the optimal camera arrangement.