Face Recognition by Combination of PCA and Gabor Filter

Methods for face recognition which are based on face structure are among techniques without supervision and produce unfavorable results in the presence of linear changes in images. PCA is a linear transform and a powerful tool for data analysis but does not produce good results for face recognition when there are non-linear changes resulting from changes in position, intensity and gesture in the face image. To overcome this problem, methods based on face features are used. Gabor filtering which can be considered as a feature based method can be used in these cases. This paper presents a new face recognition algorithm by combining PCA and Gabor filtering methods. After Gabor filtering of each face image, a number of images is produced. Then, mean of these images is calculated and PCA is applied to it. The resulted principal components are then used for face recognition. The presented algorithm has been applied to face images from YaleB and ORL databases under different conditions. Results show that the new algorithm performs better than PCA or Gabor filtering methods when they are applied to face images independently.