Multi-pose facial correction based on Gaussian process with combined kernel function
Shuyan Shi, Ruirui Ji, Fan Zhang · Ninth International Conference on Graphic and Image Processing (ICGIP 2017) · 2018
In order to improve the recognition rate of various postures, this paper proposes a method of facial correction based on Gaussian Process which build a nonlinear regression model between the front and the side face with combined kernel function. The face images with horizontal angle from -45° to +45° can be properly corrected to front faces. Finally, Support Vector Machine is employed for face recognition. Experiments on CAS PEAL R1 face database show that Gaussian process can weaken the influence of pose changes and improve the accuracy of face recognition to certain extent.