A novel approach to robust face recognition based on frequency cluster is proposed to solve the problem of robust face recognition under complex conditions. First

a continuous information sampling unit is scattered across a detected sub-region face image. Information entropy in the foreground and background regions of the sampling unit is calculated. The entropy energy and energy frequency of the sampling unit are then calculated; the weaker energy frequency is removed by filtration and the edge-of-face frequency is calculated by the second-order partial derivatives with normalized frequency coefficient. Thus

the main feature information of the face is established. Finally

the geometrical layout of each sampling unit is obtained according to the coordinate position of the sampling unit

the entropy energy

and the energy frequency. The frequency cluster model is taken as a facial feature for identification and matching

and is constructed based on entropy energy

energy frequency

and geometrical layout. The average recognition accuracy was 99.11% on FERET and ORL-Yale database

and 97.36% on CMU-PIE database. The average processing speed of a single face image was 0.077 seconds. Experiments showed that this method could overcome the effects of illumination

varied poses

and varied expressions

while taking advantage of the strong robustness of frequency cluster. The proposed approach showed good adaptability to face recognition and significantly improved the robustness of face recognition under complex conditions

such as illumination variations

feature ambiguity

pose

and expression changes.