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The goal of matching unknown faces against a gallery of known people, the face identification is necessary. There are very accurate techniques to perform face identification in controlled environments. but face identification under uncontrolled environments is still an problem. face recognition which considers both shape and texture information to represent face images.The face area is first divided into small regions from which Local Binary Pattern (LBP) histograms are extracted and concatenated into a single, spatially enhanced feature histogram efficiently representing the face image. A large and rich set of feature descriptor for face identification using partial least squares to perform uncontrolled conditions .The method is evaluated on Facial Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC) data sets .This Algorithms is performed under uncontrolled conditions such as uncontrolled lighting and changes in facial expressions, recognition rates increases Partial Least Squares (PLS) analysis, an efficient dimensionality reduction technique, one which preserves significant discriminative information, to project the data onto a much lower dimensional subspace (20 dimensions reduced from the original 170,000).

Keywords

Face Identification, Feature Combination, Feature Selection, Lbp, Partial Least Squares (PLS)

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