The aim of this paper is to demonstrate that it is possible to reconstruct coherent human faces from very degraded pixelated images with a very fast algorithm, more faster than compressed sensing (CS) algorithm, easier to compute and without deep learning, so without important information technology resources, i.e. a large database of thousands training images (see https://arxiv.org/pdf/2003.13063.pdf). This technological breakthrough has been patented in 2018 with the demand of french patent FR 1855485 (https://patents.google.com/patent/FR3082980A1). The Face Super-Resolution (FSR) has many interests, in particular in a remote surveillance context which already exists in China but which can be a reality in USA and European countries. Today, deep learning methods and artificial intelligence (AI) appears in this context but these methods are difficult to put in their systems because of the need of important data. The demand of chinese patent CN107563965 and the scientist publication "Pixel Recursive Super Resolution", R. Dahl, M. Norouzi, J. Shlens propose such methods (see https://arxiv.org/pdf/1702.00783.pdf). In this context, this new method could help governments, institutions and enterprises to accelerate the generalisation of automatic facial identification and to earn time for reconstruction process in industrial steps such as terahertz imaging, medical imaging or spatial imaging.