Quality Assessment and Restoration of Face Images in Long Range/High Zoom Video

Abstract

Most existing face related research is restricted to close range applications with low and constant system magnifications (camera zoom). To improve the performance of face recognition algorithms in wide area surveillance applica- tions, we initiate a study regarding the effects of increased system magnifi- cations and observation distances on face recognition rates (FRR). We first describe a new face video database including still face images and video se- quences from long distances (indoor: 10m-20m and outdoor: 50m-300m). The corresponding system magnification is elevated from less than 3× to 20× for indoor and up to 375× for outdoor. Deteriorations unique to high magnifica- tion and long range face images are investigated. Magnification blur proves to be a major degradation source for face recognition and is addressed via blur assessment and deblurring algorithms. Experimental results validate a relative improvement of up to 26% in FRR after assessment and restoration of high magnification face images.