In this paper

Research background of automatic face recognition and its relation to human vision system are briefly reviewed. Then current face recognition technologies are roughly introduced and classified according to different recognition features. Four main algorithms are analyzed and compared. The first is eigenface

which is extraction of global features using the PCA. In this approach

a set of faces is represented using a small number of global eigen vectors

which encode the major variations in the input set. The second is flexible model

which separate shape and gray parameter. The third is wavelet-based elastic graph matching

in which memorized faces are represented by regular graphs

whose vertices are labeled by a multi resolution description in terms of localized spatial frequencies. Spatial relationships within the object are labeled by geometrical distance vectors. The last method is traditional analytical techniques. Based on the analysis and comparison

key factors in face recognition technologies are concluded and distilled as suggestion to future research.