In this paper, we described an efficient feature pursuit scheme for boosting. The proposed method is based on the infomax principle, which seeks optimal feature that achieves maximal mutual information with class labels. Direct feature pursuit with infomax is computationally prohibitive, so an efficient gradient ascent algorithm is further proposed, based on the quadratic mutual information, non-parametric density estimation and fast Gauss transform. The feature pursuit process is integrated into a boosting framework as infomax boosting. The performance of a face detector based on infomax boosting is reported.