Single Training Sample Face Recognition Based on Gabor and 2DPCA

Jun Yang, Yanli Liu

Abstract: Single training sample face recognition problem is a challenge in face recognition field, so the distinguished feature extracting is important step for improving the recognition correct rate under the condition only one sample of one person in training set. Gabor feature and 2DPCA reducing dimension algorithm are also effective feature extracting method and are applied on face recognition and pattern analysis fields. But the two methods can’t be combined because that 2DPCA need inputting data with 2D structure. A feature extraction method based on Gabor and 2DPCA is proposed in this paper. It transforms a series of Gabor sub-images to an image with the help of image splicing technique, and then 2DPCA can be employed. Experimental results on ORL face dataset show that the proposed method is effective with higher correct rate than those of other similar algorithms for single face recognition.