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Augmented reality has gained an increasing research interest over the few last years. Customers requirements have become more intense and more demanding, the need of the different industries to re-adapt their products and enhance them by recent advances in the computer vision and more intelligence has become a necessary. In this work we present a marker-less augmented reality application that can be used and expanded in the e-commerce industry. We take benefit of the well known boosting techniques to train and evaluate different face detectors using the multi-block local binary features. The work purpose is to select the more relevant training parameters in order to maximize the classification accuracy. Using the resulted face detector, the position of the face will serve as a marker in the proposed augmented reality

Push Recovery for Humanoid Robot in Dynamic Environment and Classifying the Data Using K-Mean