Face recognition using illuminant locality preserving projections

A novel supervised manifold learning method was proposed to realize high-accuracy face recognition under varying illuminant conditions. The proposed method, named illuminant locality preserving projections (ILPP), exploited illuminant directions to alleviate the effect of illumination variations on face recognition. The face images were first projected into low-dimensional subspace. Then the ILPP translated the face images along specific direction to reduce lighting variations in the face. The ILPP reduced the distance between face images of the same class, while increase the distance between face images of different classes. This proposed method was derived from the locality preserving projections (LPP) methods, and was designed to handle face images with various illuminations. It preserved the face image’s local structure in low-dimensional subspace. The ILPP method was compared with LPP and discriminant locality preserving projections (DLPP), based on the YaleB face database. Experimental results showed the effectiveness of the proposed algorithm on the face recognition with various illuminations.