A New ExtendFace Representation Method for Face Recognition

Many traditional face recognition methods based on Fisher discriminant analysis and locally graph embedding are proposed for dimensional reduction in nonlinear data. However, these methods are not effective by using the face images with non-ideal conditions (such as, variations of expression, pose, illumination and noisy environment). That is to say, face recognition methods are difficult to achieve good performance due to the absence of appropriate and sufficient front training images. Unfortunately, most existing discriminant analysis approaches fail to work especially for single image per person problem because there is only a single training sample per person such that the within-class variation of this person cannot be calculated in such case. In this paper, we present a new face recognition method by using complex number based data augmentation. The proposed method first deals with the information provided by the original face images and obtains the new representations. Then, fuse original face images and the obtained new images into complex numbers by using a simple combination. Then, the samples can be mapped into the new representation space for classification by using the kernel function, and a test face image can be expressed by the linear combination of all the training face images. Finally, the classification predication can be completed via using collaborative representation based classification. The proposed method is abbreviated as ExtendFace. The performance of ExtendFace method is evaluated on ORL and Yale databases. Experimental results show that the ExtendFace method outperforms the other related methods in terms of recognition rates.