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This paper presents a real-time face recognition system using kinect sensor. The algorithm is implemented on GPU using opencl and significant speed improvements are observed. We use kinect depth image to increase the robustness and reduce computational cost of conventional LBP based face recognition. The main objective of this paper was to perform robust, high speed fusion based face recognition and tracking. The algorithm is mainly composed of three steps. First step is to detect all faces in the video using viola jones algorithm. The second step is online database generation using a tracking window on the face. A modified LBP feature vector is calculated using fusion information from depth and greyscale image on GPU. This feature vector is used to train a svm classifier. Third step involves recognition of multiple faces based on our modified feature vector.

This paper presents a novel approach for real time face detection using heterogeneous computing. The algorithm uses local binary pattern (LBP) as feature vector for face detection. OpenCL is used to accelerate the code using GPU[1]. Illuminance invariance is achieved using gamma correction and Difference of Gaussian(DOG) to make the algorithm robust against varying lighting conditions. This implementation is compared with previous parallel implementation and is found to perform faster.