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
1954340199 2324335215 2070653370 2108024382
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. Recently, sparse representation based classification (SRC) and collaborative representation based classification (CRC) have been successfully used for visual recognition and have demonstrated impressive performance. Given a test sample, SRC or CRC formulates its linear representation with respect to the training samples and then computes the residual error for each class. SRC or CRC assumes that the training samples from each class contribute equally to the dictionary in the corresponding class, i.e., the dictionary consists of the training samples in that class. This, however, leads to high residual error and instability. To overcome this limitation, we propose a class specific dictionary learning algorithm. To be specific, by introducing the dual form of dictionary learning, an explicit relationship between the basis vectors and the original image features is represented, which also enhances the interpretability. SRC or CRC can be thus considered as a special case of the proposed algorithm. Blockwise coordinate descent algorithm and Lagrange multipliers are then adopted to optimize the corresponding objective function. Extensive experimental results on five benchmark face recognition datasets show that the proposed algorithm achieves superior performance compared with conventional classification algorithms. HighlightsA novel class specific dictionary learning (CSDL) approach is proposed, where the standard collaborative representation based classification (CRC) or sparse representation based classification (SRC) can be considered as its special cases.The dual form of dictionary learning is proposed to enhance the interpretability.Our proposed CSDL achieves superior face recognition performance on several benchmark datasets. We merge illumination normalization and component features into the framework of Sparse Representation-based Classification (SRC) for face recognition across illumination. Unlike most SRC-based face recognition which constructs a dictionary from a training set with sufficient illumination variation, the proposed method adopts a dictionary with illumination-normalized training set. This can be the first attempt to show that illumination normalization can upgrade the performance of SRC-based face recognition. To further improve the performance, we add in schemes exploiting local features, and prove its effectiveness. Experiments on FERET and Multi-PIE databases show that the performance of the proposed method can be competitive to the state of the art. Motivated by image reconstruction, sparse representation based classification (SRC) has been shown to be an effective method for applications like face recognition. In this paper, we propose a locality-sensitive dictionary learning algorithm for SRC, in which the designed dictionary is able to preserve local data structure, resulting in improved image classification. During the dictionary update and sparse coding stages in the proposed algorithm, we provide closed-form solutions and enforce the data locality constraint throughout the learning process. In contrast to previous dictionary learning approaches utilizing sparse representation techniques, which did not (or only partially) take data locality into consideration, our algorithm is able to produce a more representative dictionary and thus achieves better performance. We conduct experiments on databases designed for face and handwritten digit recognition. For such reconstruction-based classification problems, we will confirm that our proposed method results in better or comparable performance as state-of-the-art SRC methods do, while less training time for dictionary learning can be achieved.