Robust Face Recognition in the Presence of Noises and Blurring Effects by Fusing Appearance Based Techniques and Sparse Representation

In real life, images obtained from video cameras or scanners are usually exposed to different levels of noises and blurring effects. In this paper we propose a new robust score level fusion technique to recognize faces in the presence of noise and blurring effects. The Proposed Score Level Fusion Technique (PSLFT) is obtained by using combinatory approach and Z-Score normalization using the scores obtained from appearance based techniques: Principal Component Analysis (PCA), Fisher faces (FF), Independent Component Analysis (ICA), Fourier Spectra (FS), Singular Value Decomposition (SVD) and Sparse Representation (SR). The system is trained in the absence of noise, blurring effect but tested by imposing different levels of noises and blurring effects thus we have tried to imitate the real world scenarios. To investigate the performance of PSLFT, we simulate the real world scenario by adding noises: Median noise, Salt and pepper noise and also adding blurring effects: Motion blur and Gaussian blur. To evaluate performance of the PSLFT, we have considered six standard public face databases: IITK, ATT, JAFEE, CALTECH, GRIMANCE, and SHEFFIELD.