# A Person Re-Identification Scheme Using Local Multiscale Feature Embedding with Dual Pyramids
## 2 Related Work  
Existing methods that perform re-identification based only on person images [5; 6; 7; 8; 9; 10; 11; 12] use neural networks with unique structures to construct more discriminating feature embedding by extracting as much information as possible from the given image. These methods can be classified into two broad categories depending on how the neural network selects the region of an image reflected in the feature embedding. The first method constitutes feature embedding that reflects all visual feature elements that exist in the entire image [5; 6; 7; 8], and such feature embedding is called the global feature embedding [5; 6; 7; 8]. Conversely, when multiple feature embedding is formed based on the visual elements of corresponding small area images and the small area images are considered as a combination of multiple area images when given an image, the embedding is referred to as local feature embedding [9; 10; 11; 12]. First, the neural networks of the studies using global feature embedding [5; 6; 7; 8] mostly focus on the generation of discriminative feature embedding, which denotes visual elements that are randomly distributed throughout the image. For example, in [5; 6], the input person images were reconstructed into different sizes. For example, variously sized copies with different resolutions were initially made [5], or small patch images with various sizes were separated from the pre-determined points of the foreground [6]. Subsequently, the reconstructed images were provided to the sub-networks that accept inputs suitable for differently sized images and then used to form feature embedding representing the corresponding person. This method has an advantage in that a neural network can extract and utilize a large amount of information from images of various sizes created based on a person image. This leads to exhibition of excellent performance without separate meta-information such as shooting time and location. However, as the sizes of the images used as inputs vary, several neural networks are required to accommodate images of different sizes. For example, two or more InceptionV3 networks [13] with 23.5 million in weight were used in [5], and 21 self-designed affiliated neural networks were used in [6], leading to excessively complex neural networks. In addition, adjusting previously determined resolution [5] or the separation point of the patches [6] becomes inevitable when applied to an actual environment for optimal performance. On the other hand, in [7,8], only one image was used as an input to the single neural network. Instead of any other preprocessing or auxiliary network, in [7] a unique module composed of a series of layer sets that consisted of a parallel connection of a series of one or two convolution layers was used, and in [8] one to four serially connected convolutional layers with kernels sized 3*3 each and arranged in parallel were placed within the neural network to provide a square-shaped receptive field of various sizes and reflect visual feature elements in the feature embedding. However, the important visual feature elements that can be used in identifying the person such as the person's arms, legs, and feet, clothing or patterns on it, and the person's belongings are mostly shown as rectangular shapes at various regions of the input image. Therefore, when the receptive fields of a neural network that can be given to the input image are limited to square shapes [7,8], properly reflecting visual elements of other shapes such as rectangular shapes with either longer horizontal or vertical direction can be problematic in the input embedding. Previous studies using the local feature embedding also applied auxiliary network or similar techniques to find areas of body parts as a basic position for feature embedding extraction such as arms, legs, and torso [9]. However, this method was problematic because the performance of the person re-identification could be greatly varied depending on the body part detection result.