Fuzzy Local Discriminant Embedding (FLDE) For Face Recognition

Face images are always affected by variations in illumination conditions and different facial expressions in the real world. Recently, local discriminant embedding (LDE) was proposed to manifold learning and pattern classification. LDE achieves good discriminating performance by integrating the information of neighbor and class relations between data points. But LDE cannot solve illumination problem in face recognition. So, the fuzzy local discriminant embedding (FLDE) algorithm is proposed, in which the fuzzy k-nearest neighbor (FKNN) is implemented to achieve the local distribution information of original samples. Experimental results on ORL, Yale and AR face databases show the effectiveness of the proposed method.