Super-resolution reconstruction face recognition based on multi-level FFD registration

Abstract Super-resolution (SR) reconstruction is an effective method to solve the problem, that the face image resolution is too low to be recognized in video, but the non-rigid change of deformed face and expression changes greatly affect the accuracy of registration and reconstruction. To solve these problems, a method of multi-level model free form deformation (FFD) elastic registration algorithm based on B spline is proposed. It first use low-resolution FFD grid for global registration, to emphasize the contribution of edge information for registration, we introduce edge registration measure into the sum of squared difference (SSD) criterion. Then divide the global registration image and reference image into a series of corresponding sub-image pairs and calculate the correlation coefficient of each pair; at the same time, we do local registration with high-resolution FFD grid to the small value correlation coefficient sub-image pairs. In the registration process of optimization, the paper uses adaptive step length gradient descent method algorithm based on chaotic variables to improve optimization efficiency. After registration, the algorithm of project onto convex sets (POCS) is used to reconstruct SR face image through several low resolution image sequences, and then recognized these SR face images by support vector machines (SVM) classifier. Experimental results from standard video database and self-built video database show that this method can register and reconstruct face image accurately in the condition of great face deformation and expression change, while the face recognition accuracy is also improved.