Robust real time face recognition and tracking on gpu using fusion of rgb and depth image
This work addresses the need for robust and fast face recognition in applications like surveillance or human-computer interaction, but it is incremental as it builds on existing methods like LBP and SVM.
The paper tackles real-time face recognition by fusing RGB and depth images from a Kinect sensor, achieving significant speed improvements through GPU implementation with OpenCL.
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.