Person re-identification is being widely used in the forensic, and security and surveillance system, but person re-identification is a challenging task in real life scenario. Hence, in this work, a new feature descriptor model has been proposed using a multilayer framework of Gaussian distribution model on pixel features, which include color moments, color space values and Schmid filter responses. An image of a person usually consists of distinct body regions, usually with differentiable clothing followed by local colors and texture patterns. Thus, the image is evaluated locally by dividing the image into overlapping regions. Each region is further fragmented into a set of local Gaussians on small patches. A global Gaussian encodes, these local Gaussians for each region creating a multi-level structure. Hence, the global picture of a person is described by local level information present in it, which is often ignored. Also, we have analyzed the efficiency of earlier metric learning methods on this descriptor. The performance of the descriptor is evaluated on four public available challenging datasets and the highest accuracy achieved on these datasets are compared with similar state-of-the-arts, which demonstrate the superior performance.

翻译：法医、安全和监视系统正在广泛使用人员再识别,但个人再识别在现实生活中是一项具有挑战性的任务。因此,在这项工作中,提出了一个新的特征描述模型,采用了高山像素特征分布模型的多层框架,其中包括颜色瞬间、颜色空间值和Schmid过滤器反应。一个人的图像通常由不同的身体区域组成,通常有不同的服装,然后是当地的颜色和纹理模式。因此,通过将图像分为重叠区域,对图像进行当地评价。每个区域进一步分成一组小片段的当地高山人。全球高山编码,每个区域的这些地方高山编码创建了多层结构。因此,一个人的全球图象由它所含的地方一级信息描述,而这些信息往往被忽视。此外,我们分析了先前关于该描述仪的衡量方法的效率。对描述仪的性能进行了评估,在四个公众可用的具有挑战性的数据集上进行了评估,这些数据集实现的最高精确度与类似的州性表现相比较,显示高水平。