Abstract: In order to enhance robustness of traditional Gabor features towards illumination,
expression and pose variance and overcome its high dimension problem, the paper proposes a face
recognition method based on Gabor, local binary patter and variance projection entropy improved
algorithm. First, the multi direction multi-scale fusion Gabor image is coded with LBP, and the coded
image fused and the histograms of image block calculated. Second, a local projection entropy feature
extraction is adopted for face images with anti-geometric distortion variance projection entropy and
cross variance projection entropy operator. Finally, the face recognition is completed by using BP
neutral network to fuse and make decision weightily. The G-LBP feature extraction reduces the
redundancy of data greatly, and maintains the integrity of the effective information. Variance
projection of entropy and cross entropy improves the richness of the feature. The weighted fusion in
decision-making layer plays an important role of integration between the classifiers and improves the recognition rate of face recognition. Compared with other literature algorithms, experiment results
verify the effectiveness and superiority of the proposed algorithm.

Key words: face recognition, variance projection entropy, G-LBP, BP neutral network