Face Recognition Using Parallel Algorithm

In this paper we present a novel parallel implementation of Local Binary Pattern based face recognition algorithm, improving the recognition accuracy. An adaptive block matching method is introduced in the context of the proposed algorithm for parallel image processing. The local binary pattern approach has evolved to represent significant breakthrough in texture analysis, outperforming earlier methods in many applications. Perhaps the most important property of the LBP operator in real-world applications is its tolerance against illumination changes. Another equally important is its computational simplicity, which makes it possible to analyze images in challenging real-time settings. Our excellent results suggest that that texture and the ideas behind the LBP methodology could have a much wider role in image analysis and computer vision than was thought before. Extensive experiments clearly show the superiority of the proposed scheme over all considered methods (PCA, Bayesian Intra/extrapersonal Classifier and Elastic Bunch Graph Matching) on FERET tests which include testing the robustness of the method against different facial expressions, lighting and aging of the subjects. In addition to its efficiency, the simplicity of the proposed method allows for very fast feature extraction.