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This paper proposes an analysis-by-synthesis framework for face recognition with variant pose, illumination and expression. First, an efficient 2D-to-3D integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination; Then, realistic virtual faces with different of pose, illumination and expression are synthesized based on the personalized 3D face to characterize the face subspace; Finally, face recognition is conducted based on these representative virtual faces. Compared with other related works, this framework has the following advantages; 1) only one single frontal fae is required for face recognition, which avoids the burdensome enrollment work; 2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex pose, illumination and expression; and 3) the proposed 2D-to-3D integrated face reconstruction approach is fully automatic and more efficient. From the experimental results show that the synthesized virtual faces significantly improve the accuracy of face recognition with variant pose, illumination and expression.