High-Stability Real-Time Method for Headpose Estimation Based on Nonlinear Optimization
Headpose solving is one of the key technologies in face recognition systems.In order to solve the problem of unstable attitude of a few face feature points,this paper proposes a method of attitude solving based on nonlinear optimization.Firstly,the 3D point re-projection coordinates are solved according to the camera imaging principle,and the coordinates and observations are based on the projection points.The point coordinate relationship is used to construct the least squares problem of reprojection error.Then the knowledge of Lie algebra is used to solve the Jacobian matrix of the least squares equation.The Gauss-Newton method is used to iteratively solve the minimum reprojection error in the gradient direction.Face attitude angle.The simulation and real experiments prove that the headpose information can be solved stably under the condition of 5 feature points,the accuracy is better than other algorithms,the error between the rotation angle and the actual value is reduced to 1.9％,and the translation is reduced to 1.5％.This algorithm has been applied to the face compliance detection process of actual products.Face gesture recognition is more accurate,which is about 8％better than the traditional algorithm.
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