Abstract
The popular Local binary patterns (LBP) have been highly successful in describing and recognizing faces. However, the original LBP has several limitations which must to be optimized in order to improve its performances to make it suitable for the needs of different types of problems. In this paper, we investigate a new local texture descriptor for automated human identification using 2D facial imaging, this descriptor, denoted: One Dimensional Local Binary Pattern (1DLBP), produces binary code and inspired from classical LBP. The performances of the textural descriptor have been improved by the introduction of the wavelets in order to reduce the dimensionalities of the resulting vectors without losing information. The 1DLBP descriptor is assessed in comparison to the classical and the extended versions of the LBP descriptor. The experimental results applied on two publically datasets, which are the ORL and AR databases, show that this proposed approach of feature extraction, based on 1DLBP descriptor, given very significant improvements at the recognition rates, superiority in comparison to the state of the art, and a good effectiveness in the unconstrained cases.
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