A fuzzy approximator with Gaussian membership functions to estimate a human´s head pose

Estimating the head pose plays an important role in computer vision and also as a key task for visual surveillance and face recognition applications hence a prominent problem in computer vision. Most of the works in this field suffer from lack of continuous estimating of the head pose and high accuracy. We know fuzzy systems as universal approximator capable of approximating an unknown function by having just few limited information while attaining high accuracy. In this paper, an improved approach is proposed for estimating the rotation angle of the head along horizontal axis based on a fuzzy approximator in which the membership functions are of Gaussian type. The proposed method is able to provide a continuous estimate of the head along horizontal axis with high accuracy, low computational cost while avoiding from getting involved into complex mathematical equations. Experiments on images from two standard well-known databases showed less than 6° of average absolute error in estimation which is a significant improvement over the approximator with simple triangular membership functions.