Feature extraction based on fuzzy local discriminant embedding with applications to face recognition

Recently, local discriminant embedding (LDE) was proposed to manifold learning and pattern classification. LDE achieves good discriminating performance by integrating the information of neighbour and class relations between data points. However, in the real-world applications, the performances of face recognition are always affected by variations in illumination conditions and different facial expressions. LDE still cannot solve illumination problem in face recognition. In this study, the fuzzy local discriminant embedding (FLDE) algorithm is proposed, in which the fuzzy k-nearest neighbour (FKNN) is implemented to reduce these outer effects to obtain the correct local distribution information to persuit good performance. In the proposed method, a membership degree matrix is firstly calculated using FKNN, then the membership degree is incorporated into the definition of the Laplacian scatter matrix to obtain the fuzzy Laplacian scatter matrix. The optimal projections of FLDE can be obtained by solving a generalised eigenfunction. Experimental results on ORL, Yale and AR face databases show the effectiveness of the proposed method.