Multiple unconstrained observations of the same object can be easily accessed by the Internet

with regard to overcoming the identification-difficult of the unconstrained samples.Moreover

to exploit the information of multiple observation sets to improve the classification performance

a multiple observation sets classification algorithm based on joint dynamic spare representation of low-rank decomposition is presented.First of all

we need find the best set of image transform domain

which decomposes the data matrix into a low-rank matrix and an associated sparse error matrix.Secondly

the low-rank matrix and sparse error matrix is represented by joint dynamic sparsity respectively

in order to make full use of the correlation of the class-level and the differences of the atom-level

i.e

the sparse representation vectors for the multiple observations can share the same class-level sparsity pattern while their atom-level sparsity patterns may be distinct.Finally

we compare the classification results with the total sparse reconstruction errors.Three comparative experiments are conducted on CMU-PIE face dataset

ETH-80 object recognition dataset

USPS handwritten digit dataset

and UMIST face dataset

and the results demonstrate the superiority of the proposed algorithm.