Recognizing faces when images are corrupted by varying degree of noises and blurring effects

Abstract

Most images are corrupted by various noises and blurring effects. Recognizing human faces in the presence of noises and blurring effects is a challenging task. Appearance based techniques are usually preferred to recognize faces under different degree of noises. Two state of the art techniques considered in our paper are Locality Preserving Projections (LPP) and Hybrid Spatial Feature Interdependence Matrix (HSFIM) based face descriptors. To investigate the performance of LPP and HSFIM we simulate the real world scenario by adding noises: Gaussian noise, Salt and pepper noise and also adding blurring effects: Motion blur and Gaussian blur on six standard public face databases: IITK, ATT, JAFEE, CALTECH, GRIMANCE, and SHEFFIELD.