Pedestrian re-identification generally considered as a sub-problem of image retrieval. Due to the distance between the camera and the pedestrian, the definition of the pedestrian photo is generally fuzzy, and the camera’s view angle of pedestrians is fixed, so it is not enough to recognize pedestrians by faces. In order to better mine strong local features and improve the accuracy of pedestrian re-identification, this study proposes an algorithm, namely Horizontal Pooling for Local Feature (HPLF). We preprocess the input joint data set in ResNet-50 network, extract features, and horizontally cut the feature map generated by ResNet-50 network, with which we calculate the distance between every two features. Triple loss with hard example mining (TriHard loss) is used for training as a local feature loss function. The global distance is calculated according to the feature map and trained through TriHard loss. The two loss functions plus a Softmax cross entropy loss function are combined as the total loss function for parameter correction. The experimental results show that HPLF’s performances of mean Average Precision (mAP), Rank-1, Rank-5, and Rank-10 in the Market1501 data set are about 3% higher than those of other algorithms.