Fast L1-eigenfaces for robust face recogntion

Face recognition using eigenfaces is a popular technique based on principal component analysis (PCA). However, its performance suffers from the presence of outliers due to occlusions and noise often encountered in unconstrained settings. We address this problem by utilizing L₁-eigenfaces for robust face recognition. We introduce an effective approach for L₁-eigenfaces based on combining fast computation of L₁-PCA with a greedy search technique. Experimental results demonstrate that L₁-eigenfaces outperform traditional L₂-eigenfaces for face recognition and reconstruction on the Yale face database corrupted with random occlusions.