This paper proposes a new version of LBP and its inclusion into covariance region descriptors for image matching and recognition. Starting from the non-rotation invariant uniform LBP (called nriLBP), the pattern is described by the cosine and sine values of the angular portion defined by the 1s. The use of this four-value vector leads to a better resilience of the feature to noise and small neighborhood rotations. Several color versions of this feature are proposed. For region description, these local features are included in covariance matrices, noted ELBCM for Enhanced-LBP Covariance Matrix. Experimental evaluations confirm the relevance of the proposed models on three databases designed for texture analysis, object retrieval and person re-identification. A study is also made on the impact of the colorspace included in the covariance descriptor and used for LBP definition. The experiments show that ELBCM has better recognition performance than the 12 other descriptors tested.