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Person re-identification addresses the problem of pedestrian image matching in a nonoverlapped surveillance network. Traditional metric learning methods try to learn a fixed pedestrian images matching metric model. However, existing metric learning-based methods have the problem of overfitting the training data. In order to solve this problem, a sample-specific metric learning-based method is proposed. In the perspective of probability distribution, the over-fitting problem is attributed to the problem that the generalisation ability of projection features is weak. Firstly, the proposed method learns a metric subspace, which projects the raw feature to a discriminative subspace. Then, a projection feature selection method is established based on the probability distribution of positive image pairs. According to the proposed method, the probability that the test samples following the training data distribution is closely related to the adaptability. The proposed projection feature selection method selects different projection features for each individual's similarity distance measure. Finally, extensive experiments on four published datasets verify the effectiveness of the proposed metric learning method. It performs favourably against the comparison methods, especially on the rank-1 rate.

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