One major challenge for face recognition techniques is the difficulty of collecting image samples. More samples usually mean better results but also more effort, time, and thus money. Unfortunately, many current face recognition techniques rely heavily on the large size and representativeness of the training sets, and most methods suffer degraded performance or fail to work if there is only one training sample per person available. This so-called "Single Sample per Person" (SSP) situation is common in face recognition. To resolve this problem, we propose a novel approach based on a combination of Gabor Filter, Local Binary Pattern and Whitened PCA (LGBPWP). The new LGBPWP method has been successfully implemented and evaluated through experiments on 3000+ FERET frontal face images of 1196 subjects. The results show the advantages of our method - it has achieved the best results on the FERET database. The established recognition rates are 98.1%, 98.9%, 83.8% and 81.6% on the fb, fc, dup I, and dup II probes, respectively, using only one training sample per person.