Sparse representation-based classification (SRC), proposed by , seeks the sparsest decomposition of a test sample over the dictionary of training samples, with classification to the most-contributing class. Because it assumes test samples can be written as linear combinations of their same-class training samples, the success of SRC depends on the size and representativeness of the training set. Our proposed classification algorithm enlarges the training set by using local principal component analysis to approximate the basis vectors of the tangent hyperplane of the class manifold at each training sample. The dictionary in SRC is replaced by a local dictionary that adapts to the test sample and includes training samples and their corresponding tangent basis vectors. We use a synthetic data set and three face databases to demonstrate that this method can achieve higher classification accuracy than SRC in cases of sparse sampling, nonlinear class manifolds, and stringent dimension reduction.
Other classification algorithms have been proposed that are similar to ours in that they aim to enlarge or otherwise enhance the training set in SRC. Such methods for face recognition, for example, include the use of virtual images that exploit the symmetry of the human face, as in both the method of @cite_18 and @cite_34 . Though visual comparison of these virtual images and our recovered tangent vectors (see Section ) could be informative, our proposed method can be used for general classification.
@cite_18 @cite_34
2117286860 1954340199
The face almost always has an axis-symmetrical structure. However, as the face usually does not have an absolutely frontal pose when it is imaged, the majority of face images are not symmetrical images. These facts inspire us that the mirror image of the face image might be viewed as a representation of the face with a possible pose opposite to that of the original face image. In this paper we propose a scheme to produce the mirror image of the face and integrate the original face image and its mirror image for representation-based face recognition. This scheme is simple and computationally efficient. Almost all the representation-based classification methods can be improved by this scheme. The underlying rationales of the scheme are as follows: first, the use of the mirror image can somewhat overcome the misalignment problem of the face image in face recognition. Second, it is able to somewhat eliminate the side-effect of the variation of the pose and illumination of the original face image. The experiments show that the proposed scheme can greatly improve the accuracy of the representation-based classification methods. The proposed scheme might be also helpful for improving other face recognition methods. We proposed a method to use virtual available facial images for face recognition.We improved the linear regression classification method for face recognition.The classification accuracy on sample pairs are better than that on original samples.This method achieves lower classification error rates than many other methods.This method performs well even when there are few training samples of each class. Sparse representation classification, as one of the state-of-the-art classification methods, has been widely studied and successfully applied in face recognition since it was proposed by In this study, we proposed a method to generate virtual available facial images and modified the well-known linear regression classification (LRC) and collaborative representation based classification (CRC) for face recognition. The new method integrates the original and virtual symmetry facial images to form a training sample set of large size. Experimental results show that the proposed method can achieve better performance than most of the competitive face recognition methods, e.g. LRC, CRC, INNC, SRC, RCR, RRC and the method in (2014). This promising performance is mainly attributed to the fact that the sample combination scheme used in the new method can exploit limited original training samples to produce a large number of available training samples and to convey sufficient variations of the original training samples.