Person re-identification plays important roles in many practical applications. Due to various human poses, complex backgrounds and similarity of person clothes, person re-identification is still a challenging task. In this paper, we mainly focus on the robust and discriminative appearance feature representation and proposed a novel multi-appearance method for person re-identification. First, we proposed a deep feature fusion method and get the multi-appearance feature by combining two Convolutional Neural Networks. Then, in order to further enhance the representation of the appearance feature, the multi-part model was constructed by combining the whole body and the six body parts. Additionally, we optimized the feature extraction process by adding a pooling layer. Comprehensive and comparative experiments with the state-of-the-art methods over publicly available datasets demonstrated that the proposed method can get promising results.

人员重新识别在许多实际应用中起着重要作用。由于各种人体姿势，复杂的背景和人的衣服的相似性，人的重新识别仍然是一项艰巨的任务。在本文中，我们主要关注鲁棒和可区分的外观特征表示，并提出了一种新颖的多外观人识别方法。首先，我们提出了一种深度特征融合方法，并通过结合两个卷积神经网络来获得多外观特征。然后，为了进一步增强外观特征的表示，通过将整个身体和六个身体部位结合起来，构造了多部分模型。此外，我们通过添加池化层优化了特征提取过程。