A Comparative Study on Score Level Fusion Techniques and MACE Gabor Filters for Face Recognition in the presence of Noises and Blurring effects

Face recognition has been an intensely researched field of computer vision for the past couple of decades. Though significant strides have been made in tackling the problem in controlled domains, significant challenges remain in solving it in the unconstrained domain. Two such scenarios are while recognizing faces acquired from distant cameras and when images are corrupted. The main factors that make this a challenging problem are image degradations due to noise and blur. In this paper we have developed and analyzed Score Level Fusion Technique (SLFT) of appearance based techniques and Minimum Average Correlation Energy (MACE) Gabor filter for face recognition in the presence of various noises and blurring effects. In SLFT the scores are obtained by using combinatory approach and Z-Score normalization of appearance based techniques: Principal Component Analysis (PCA), Fisherfaces (FF), Independent Component Analysis (ICA), Fourier Spectra (FS), Singular Value Decomposition (SVD) and Sparse Representation (SR). MACE Gabor filter is designed to minimize the average correlation energy (ACE) of the correlation outputs due to the training images while simultaneously satisfying the correlation peak constraints at the origin. The effect of minimizing the ACE is that the resulting correlation planes would yield values close to zero everywhere except at the location of a trained object, where it would produce a strong peak. 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 compare the performance of SLFT and MACE Gabor filter, we have considered six standard public face databases: IITK, ATT, JAFEE, CALTECH, GRIMANCE, and SHEFFIELD.