Pose variation is one of challenging problems in face recognition. Complete Pose Binary SIFT(CPBS) [1]has been proposed to extract binary SIFT from face images of five poses. It could resolve the problem of large pose variation. However, in CPBS, each feature is represented as a bit, it possibly brings about information loss. In this paper, we propose maximum bit average entropy algorithm (MBAE) for binarization of CPBS features. And these features are used for face recognition with pose variation. It preserve at most information with the fewer quantization bits. Firstly, the CPBS features are extracted from the gallery images. Secondly, the CPBS features are binarized according to the maximum bit average entropy adaptively. Finally, the distance between a pair of features is computed based on the weighted hamming distance. The compared experimental results on the CMU-PIE and FERET face databases show that our approach is much better than state-of-the-art algorithms.