Zhang H.,Harbin Institute of Technology |
Zhang Z.,A+ Network |
Li Z.,A+ Network |
Chen Y.,A+ Network |
And 2 more authors.
Journal of Modern Optics
The sparse representation classification (SRC) method proposed by Wright et al. is considered as the breakthrough of face recognition because of its good performance. Nevertheless it still cannot perfectly address the face recognition problem. The main reason for this is that variation of poses, facial expressions, and illuminations of the facial image can be rather severe and the number of available facial images are fewer than the dimensions of the facial image, so a certain linear combination of all the training samples is not able to fully represent the test sample. In this study, we proposed a novel framework to improve the representation-based classification (RBC). The framework first ran the sparse representation algorithm and determined the unavoidable deviation between the test sample and optimal linear combination of all the training samples in order to represent it. It then exploited the deviation and all the training samples to resolve the linear combination coefficients. Finally, the classification rule, the training samples, and the renewed linear combination coefficients were used to classify the test sample. Generally, the proposed framework can work for most RBC methods. From the viewpoint of regression analysis, the proposed framework has a solid theoretical soundness. Because it can, to an extent, identify the bias effect of the RBC method, it enables RBC to obtain more robust face recognition results. The experimental results on a variety of face databases demonstrated that the proposed framework can improve the collaborative representation classification, SRC, and improve the nearest neighbor classifier. © 2014 Taylor & Francis. Source
Lu Y.,Harbin Institute of Technology |
Fei L.,Harbin Institute of Technology |
Chen Y.,Shenzhen Sunwin Intelligent Co.
Proceedings of 2014 International Conference on Smart Computing, SMARTCOMP 2014
In different color spaces, the three color channels might have different relationship, but most of color face recognition methods exploit the color information in a simple way. In this paper, we propose a novel hybrid fusion scheme for color face recognition, which first uses two-phase test sample representation (TPTSR) to obtain matching scores of each color channel of the test sample and then uses the hybrid fusion scheme to combine these three kinds of matching scores for classification of the test sample. The hybrid fusion scheme exploits low- and high-order components of three kinds of matching scores based on the sum and product rule. Scores from each color channel generated from TPTSR includes both little correlated and very correlated scores, to extract low- and high-order components of these scores will allow them to be well integrated and used for classification. For evaluating the proposed method, we not only make a comparison of our method with some global and local methods such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel PCA (KPCA), kernel LDA (KLDA), locality preserving projection (LPP) and TPTSR. We also make a comparison of our method with some recently proposed local feature based methods, such as color local Gabor wavelets (CLGW), color local binary pattern (CLBP) and tensor discriminant color space (TDCS). © 2014 IEEE. Source
Zhang Z.,Harbin Institute of Technology |
Wang L.,Harbin Institute of Technology |
Zhu Q.,Nanjing University of Aeronautics and Astronautics |
Liu Z.,Henan University of Science and Technology |
And 2 more authors.
In this paper, we propose a novel noise modeling framework to improve a representation based classification (NMFIRC) method for robust face recognition. The representation based classification method has evoked large repercussions in the field of face recognition. Generally, the representation based classification method (RBCM) always first represents the test sample as a linear combination of the training samples, and then classifies the test sample by judging which class leads to a minimum reconstruction residual. However, RBCMs still cannot ideally resolve the face recognition problem owing to the varying facial expressions, poses and different illumination conditions. Furthermore, these variations can immensely influence the representation accuracy when using RBCMs to perform classification. Thus, it is a crucial problem to explore an effective way to better represent the test sample in RBCMs. In order to obtain a highly precise representation metric, the proposed framework first iteratively diminishes the representation noise and achieves better representation solution of the linear combination until it converges, and then exploits the determined 'optimal' representation solution and a fusion method to perform classification. Extensive experiments demonstrated that the proposed framework can simultaneously notably improve the representation capability by decreasing the representation noise and improve the classification accuracy of RCBM. © 2014 Elsevier B.V. Source
Wen J.,Harbin Institute of Technology |
Wen J.,A+ Network |
Chen Y.,Harbin Institute of Technology |
Chen Y.,Shenzhen Sunwin Intelligent Co. |
And 2 more authors.
Sparse representation uses all training samples to represent a test sample only once, which can be regarded as a one step representation. However, in palmprint recognition, the appearances of palms are highly correlated which means the information provided by all the training samples are redundant while using the representation-based methods. Hence, how to obtain suitable samples for representation deserves exploring. In this paper, we devise a multi-step representation manner to extract the most representative samples for representation and recognition. In addition, the proposed sample selection strategy is based on contributions of the classes, not merely the effort of a single sample. Compared with some other appearance-based methods, the proposed method obtained a competitive result on PolyU multispectral palmprint database. © 2013 Elsevier GmbH. Source
Zhu Q.,Harbin Institute of Technology |
Zhu Q.,University of Shanghai for Science and Technology |
Zhu Q.,A+ Network |
Li Z.,Harbin Institute of Technology |
And 7 more authors.
Minimum squared error based classification (MSEC) method establishes a unique classification model for all the test samples. However, this classification model may be not optimal for each test sample. This paper proposes an improved MSEC (IMSEC) method, which is tailored for each test sample. The proposed method first roughly identifies the possible classes of the test sample, and then establishes a minimum squared error (MSE) model based on the training samples from these possible classes of the test sample. We apply our method to face recognition. The experimental results on several datasets show that IMSEC outperforms MSEC and the other state-of-the-art methods in terms of accuracy. © 2013 Zhu et al. Source