Face and arm-posture recognition for secure human-machine interaction

In this paper, we present a user identification technique based on face recognition for secure human-machine interaction. User´s face is matched with the faces memorized by the machine, and if a match is found with a reliable matching score, the machine gets ready to accept the commands. Gabor Wavelet Transform coefficients are used as features for matching, and they are computed on a dedicated LSI to attain high computational speed. For matching, an algorithm based on the elastic graph matching is used, and that is also implemented on hardware. We also propose an arm tracking algorithm for communication with machines using arm gesture. The algorithm utilizes stereo vision to define search regions in left and right images iteratively, and looks for the arm posture by matching with a three dimensional four degrees-of-freedom kinematics-based arm model. Tracking procedure is computationally efficient, robust to small occlusions, and works in unconstrained environment, which makes it suitable for general applications in human-machine interaction.