Information fusion of multi-modal biometrics has attracted much attention in recent years. However, this paper focuses on the information fusion in single models, that is, the face biometric. Two different representation methods, gray level intensity and Gabor feature, are exploited for fusion. We study the fusion problem in face recognition at both the face representation level and the confidence level. At the representation level, both the PCA feature fusion and the LDA feature fusion are considered, while at the confidence level, the sum rule and the product rule are investigated. We show through experiments on FERET face database and our own face database that appropriate information fusion can improve the performance of face recognition and verification. This suggests that gray level intensity and Gabor feature compensate for each other, based on the feasible fusion.