  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.
