Face Detection and Recognition using Local Binary Patterns

Abstract

Now a days, applications in the field of surveillance, banking and multimedia equipment are becoming more important, but since each application related to face analysis demands different requirements on the analysis process, almost all algorithms and approaches for face analysis are application dependent and a standardization or generalization is quite difficult. For that reason and since many key problems are still not completely solved, the face analysis research community is still trying to cope with face detection and recognition challenges. Local Binary Patterns were first used in order to describe ordinary textures[1] and, since a face can be seen as a composition of micro textures depending on the local situation, it is also useful for face description. The LBP descriptor consists of a global texture and a local texture representation calculated by dividing the image into blocks and computing the texture histogram for each one. The global is used for discriminating the most non-face objects (blocks), whereas the second provides specific and detailed face information which can be used not only to select faces, but also to provide face information for recognition[2].The results will be concatenated in a general descriptor vector, that will be later used to feed an adequate classifier or discriminative scheme to decide the face likeness of the input image or the identity of the input face in case of face recognition.

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Cite this paper

@inproceedings{Kumar2016FaceDA, title={Face Detection and Recognition using Local Binary Patterns}, author={Amit Kumar and Maitreyee Dutta}, year={2016} }