A robust and efficient method for face recognition using phase only correlation (POC) is proposed in this paper. To achieve efficient recognition rate, it uses the concept of histogram of Gabor phase pattern (HGPP) supplemented by POC technique. In HGPP, the quadrantbit codes are first extracted from faces, and in order to encode the phase variations, global Gabor phase pattern (GGPP) and local Gabor phase pattern (LGPP) are derived. GGPP and LGPP are then split into the non-overlapping rectangular regions. From the above regions, spatial histograms are extracted and concatenated into an extended histogram feature to represent the original image. The recognition is carried out with the nearest-neighbor classifier, using the histogram intersection as the similarity measurement. Finally, face patterns are verified with POC based matching technique to improve the accuracy of the system. This method improves the result both distribution wise and content wise. Experiments are done on the large scale ORL, YALE, FERET and DCSKU databases. Experimental results show that the proposed method is promising and is comparable with the advanced face recognition algorithms reported in the literature.