L1-Grassmann manifolds for robust face recognition

Abstract

Classification on Grassmann manifolds has found application in computer vision problems because it yields improved accuracy and fast computation times. Grassmann manifolds map subspaces to single points, which involves solving for a unit vector representation that is obtained using principal component analysis (PCA). However, PCA may suffer from the presence of outliers due to noise and occlusions often encountered in unconstrained settings. We address this problem by introducing L1-Grassmann manifolds where L1-PCA is used for subspace generation during the mapping process. We utilize a new approach to L1-PCA and demonstrate the effectiveness of L1-Grassmann manifolds for robust face recognition. Results using the Yale face database and the ORL database of faces show that L1-Grassmann manifolds outperform traditional L1-Grassmann manifolds for face recognition and are more robust to noise and occlusions.