Unconstrained Face Recognition From Blurred and Illumination with Pose Variant Face Image Using SVM
Face recognition has been an intensely researched field of computer vision for the past couple of decades. Motivated by the problem of remote face recognition, this paper has addressed the problem of recognizing blurred and poorly illuminated faces. This paper has shown that the set of all images obtained by blurring a given image is a convex set given by the convex hull of shifted versions of the image. Based on this set-theoretic characterization, the work proposed a blur-robust face recognition algorithm DRBF. This algorithm can easily incorporate prior knowledge on the type of blur as constraints. Using the low-dimensional linear subspace model for illumination then showed that the set of all images obtained from a given image by blurring and changing its illumination conditions is a bi-convex set. Again, based on this set-theoretic characterization, this paper proposed a blur and illumination robust algorithm IRBF. The face under different pose can be detected and normalized by using affine transformation parameters to align the input pose image to frontal view. After completing the aforementioned pose normalization process, the resulting final image undergoes illumination normalization. This is performed using the SQI algorithm. Then face can be recognized using incorporating blur and illumination by classifying training and testing data by using SVM.