Facial images dimensionality reduction and recognition by means of 2DKLT

Paper presents an efficient dimensionality reduction method for images (e.g. human faces databases). It does not require any usual pre-processing stage (like down-scaling or filtering). Its main advantage is associated with efficient representation of images leading to accurate recognition. Analysis is performed using two-dimensional Principal Component Analysis and Linear Discriminant Analysis and reduction by means of two-dimensional Karhunen-Loeve Transform. The paper presents mathematical principles together with some results of recognition experiments on popular facial databases. The experiments performed on several facial image databases (BioID [11], ORL/AT&T [3], FERET [8], Face94 [4] and Face95 [5]) showed that face recognition using this type of feature space dimensionality reduction is particularly convenient and efficient, giving high recognition performance.