Biometrics provides a secure method of authentication and identification. Biometric data are difficult to replicate and steal. Unique identifiers include fingerprints, hand geometry, earlobe geometry, retina and iris patterns, voice waves, DNA, and signatures. This paper is based on Periocular biometric recognition, which is the appearance of the region around the eye. Periocular recognition may be useful in applications where it is difficult to obtain a clear picture of an iris for iris biometrics or a complete picture of a face for face biometrics. Acquisition of the Periocular biometrics does not require high user cooperation and close capture distance. This region usually encompasses the eyelids, eyelashes, eyebrows and the neighbouring skin area. Periocular biometrics encompasses the information of face recognition and iris recognition system. A record of a person’s unique characteristics is captured and kept in a database. Later on, when identification verification is required, a new record is captured and compared with the previous record in the database. If the data in the new record matches that in the database record, the person’s identify is confirmed. In this paper, the Local Binary Pattern (LBP) and Gray Level Co- occurrence Matrix (GLCM) are used for the feature extraction on the Periocular images. LBP is a type of feature used for classification in computer vision and a powerful feature for texture. For an effective classification and recognition of an authorized individual Back propagation neural network (BPNN) classifier is used.
