Human Identification with Face Detection Using Vector Segmentation

Abstract

We propose a new way to solve a very general blind inverse problem of multiple simultaneous degradations, such as blur, resolution reduction, noise, and contrast changes, without explicitly estimating the degradation. The proposed system is depends on combining semantic non-rigid patches, problem-specific high-quality prior data, and non-rigid registration tools. We exhibit how a significant quality enhancement can be achieved, both visually and quantitatively, in the facial images. The method is determined on the problem of cellular photography quality enhancement of dark facial images for different identities, expressions, and poses, and it is compared with the state-of-the-art denoising, deblurring, super-resolution, and color-correction methods.