多级FFD配准视频人脸超分辨率重建

超分辨率重建是解决视频人脸识别中人脸分辨率低的有效方法,但由于人脸畸变、表情变化等非刚性变化导致无法精确配准和重建.针对此问题,提出基于B样条的多级模型自由形式形变(FFD)弹性配准算法.先用低分辨率FFD网格全局配准,再对全局配准后的图像分块并计算对应子图块的相关性系数,对相关性系数小的子图块用高分辨率FFD网格局部细配准.在配准的寻优过程中采用基于混沌因子的自适应步长最速下降法提高寻优效率.配准后,采用POCS算法对多帧图像重建高分辨率图像来识别.在标准视频库和自建视频库上实验仿真,结果表明在人脸畸变和表情变化很大的情况下,也能够精确的配准和很好的重建,得到较高识别率.

Video Face Super-resolution Reconstruction Based on Multi-level FFD Registration

Super-resolution reconstruction is an effective method to solve the problem that the face image resolution is too low to recognition in video, but the non-rigid change of face distortion 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 uses low-resolution FFD grid for global registration, then divides the global registration image into several sub-images pairs and calculates the correlation coefficient of each pairs. At the same time, local registration with high-resolution FFD grid is done when the correlation coefficient of sub-image is small. In the process of optimization, adaptive step length gradient descent algorithm based on chaotic variables is used to improve optimization efficiency. After registration, the algorithm of POCS is used to reconstruct high resolution image through several low resolution images to realize recognition. Experimental results from standard video database and self-built video database show that this method can register and reconstruct accurately in the condition of great facial distortion and expression variation, and the face recognition accuracy is also improved.