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A human face conveys a lot of information about the identity and emotional state of the person. So now a day’s face recognition has become an interesting and challenging problem. Face recognition plays a vital role in many applications such as authenticating a person, system security, verification and identification for law enforcement and personal identification among others. So our research work mainly consists of three parts, namely face representation, feature extraction and classification. The first part, Face representation represents how to model a face and check which algorithms can be used for detection and recognition purpose. In the second phase i.e. feature extraction phase we compute the unique features of the face image. In the classification phase the computed DLBP face image is compared with the images from the database. In our research work, we use Double Coding Local Binary Patterns to evaluate face recognition which concentrate over both the shape and texture information to represent face images for person independent face recognition. The face area is firstly cut into small regions from which Local Binary Patterns (LBP), then we compute histograms to generate LBP image then we compute single oriented mean image from which we again compute histogram values small regions and at last concatenated into a single feature vectors and generate D-LBP image. This feature are used for the representation of the face and to measure similarities between images.

AN IMPROVED DOUBLE CODING LOCAL BINARY PATTERN ALGORITHM FOR FACE RECOGNITION