# Person Re-Identification by Low-Dimensional Features and Metric Learning
###### Abstract  
Person re-identification (Re-ID) has attracted attention due to its wide range of applications. Most recent studies have focused on the extraction of deep features, while ignoring color features that can remain stable, even for illumination variations and the variation in person pose. There are also few studies that combine the powerful learning capabilities of deep learning with color features. Therefore, we hope to use the advantages of both to design a model with low computational resource consumption and excellent performance to solve the task of person re-identification. In this paper, we designed a color feature containing relative spatial information, namely the color feature with spatial information. Then, bidirectional long short-term memory (BLSTM) networks with an attention mechanism are used to obtain the contextual relationship contained in the hand-crafted color features. Finally, experiments demonstrate that the proposed model can improve the recognition performance compared with traditional methods. At the same time, hand-crafted features based on human prior knowledge not only reduce computational consumption compared with deep learning methods but also make the model more interpretable. person re-identification; hand-crafted features; color feature; BLSTM +
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Color feature is one of the most commonly used features in traditional methods, which mostly extract color features from the \"hue, saturation, value (HSV)\" color space. However, due to the illumination variation and lack of spatial information, the traditional methods are not robust. Although most recent studies ignore color features and focus on deep features, color features still have an important research significance for person re-identification tasks regarding low-resolution images; most person pictures have a resolution of less than 300 \(\times\) 300, as shown in Table 1, and contain much less information about a person, which may make the neural network learn many useless features that not only affect the computational efficiency but also cause a waste of resources. Therefore, in this paper we propose a color feature with spatial information, which include not only person HSV color space features, but also space information. Based on the color feature, we implement bidirectional long short-term memory (BLSTM) networks with an attention mechanism to obtain the contextual relationship contained in the spatial color feature. The experimental results demonstrate that the proposed model can improve the recognition performance compared with traditional methods. At the same time, hand-crafted features based on human prior knowledge not only reduce computational consumption compared with deep learning methods but also make the model more interpretable. An overview of our proposed model is shown in Figure 1. The main contributions and novelty of this paper are as follows:  
* Based on the prior knowledge of humans, we designed a color feature with spatial information to solve the problem of person re-identification. Compared with the common method of extracting deep features using convolutional neural networks, the biggest advantage of the handcraft features we designed is that it consumes less computing resources during extraction, has better interpretability, and is less affected by image resolution. * Att-BLSTM is used to obtain the contextual semantic relationship in the color features, and due to the attention mechanism, the model can automatically focus on the features that are decisive for the task. The performance of the model and its generalization ability can be greatly improved.