In this paper, a novel approach for head pose estimation in gray-level images is presented. In the proposed algorithm, two techniques were employed. In order to deal with the large set of training data, the method of Random Forests was employed; this is a state-of-the-art classification algorithm in the field of computer vision. In order to make this system robust in terms of illumination, a Binary Pattern Run Length matrix was employed; this matrix is combination of Binary Pattern and a Run Length matrix. The binary pattern was calculated by randomly selected operator. In order to extract feature of training patch, we calculate statistical texture features from the Binary Pattern Run Length matrix. Moreover we perform some techniques to real-time operation, such as control the number of binary test. Experimental results show that our algorithm is efficient and robust against illumination change.

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To enhance a hand gesture recognition system, we compare performance in accordance with various parameters. We present an efficient framework for gesture recognition that can be easily implemented with low computational costs. Based on the simple K-NN classifier, we develop a pattern matching method through combining the Dynamic Time Warping (DTW) alignment and distance measure for similarity between two sequences. In this process, we extract various features of hand and apply various distance measure for similarity. In addition to the gesture features and distance measures, we proposed preprocessing method to enhance the performance.