  Human face recognition has been a long standing problem in computer vision
and pattern recognition. Facial analysis can be viewed as a two-fold problem,
namely (i) facial representation, and (ii) classification. So far, many face
representations have been proposed, a well-known method is the Local Binary
Pattern (LBP), which has witnessed a growing interest. In this respect, we
treat in this paper the issues of face representation as well as classification
in a novel manner. On the one hand, we use a variant to LBP, so-called Complete
Local Binary Pattern (CLBP), which differs from the basic LBP by coding a given
local region using a given central pixel and Sing_ Magnitude difference.
Subsequently, most of LBPbased descriptors use a fixed grid to code a given
facial image, which technique is, in most cases, not robust to pose variation
and misalignment. To cope with such issue, a representative Multi-Resolution
Histogram (MH) decomposition is adopted in our work. On the other hand, having
the histograms of the considered images extracted, we exploit their sparsity to
construct a so-called Sparse Representation Classifier (SRC) for further face
classification. Experimental results have been conducted on ORL face database,
and pointed out the superiority of our scheme over other popular
state-of-the-art techniques.
