A discriminative and robust feature--Kernel enhanced informative Gabor feature is proposed in this paper for face recognition. Mutual information is applied to select a set of informative and non-redundant Gabor features, which are then further enhanced by Kernel methods for recognition. When compared with an approach using the downsampled Gabor features, our methods introduce advantages on computation, memory cost and accuracy. The proposed method has also been fully tested on the FERET database according to the evaluation protocol, significant improvements on the test set is observed. Compared with the classical Gabor feature extraction approach using complex convolution process, our method requires less than 4ms to retrieve a few hundreds of features. Due to the substantially reduced feature dimension, only 4 seconds are required to recognize 200 face images.