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| 1 | Downsampling sparse representation and discriminant information aided occluded face recognition显示文摘In this paper,a strategy is proposed to deal with a challenging research topic,occluded face recognition.Our approach relies on sparse representation on downsampled input image to first locate unoccluded face parts,and then exploits the linear discriminant ability of those pixels to identify the input subject.The advantages and novelties of our method include,1)since the sparse representation based occlusion detection is conducted on dowsampled image,our algorithm is much faster than classic SRC;2)the discriminant information learned from training samples is combined with sparse representation to recognize occluded face for the first time.The verification experiments are conducted on both simulated block occlusion images and genuine occluded images. | LI YueLong MENG Li FENG JuFu WU JiGang | 2014 | Science China(Information Sciences)2014，57，3： | 5 |
| 2 | Robust sparse representation based face recognition in an adaptive weighted spatial pyramid structure显示文摘The sparse representation based classification methods has achieved significant performance in recent years. To fully exploit both the holistic and locality information of face samples, a series of sparse representation based methods in spatial pyramid structure have been proposed. However, there are still some limitations for these sparse representation methods in spatial pyramid structure. Firstly, all the spatial patches in these methods are directly aggregated with same weights, ignoring the differences of patches' reliability. Secondly, all these methods are not quite robust to poses, expression and misalignment variations, especially in under-sampled cases. In this paper, a novel method named robust sparse representation based classification in an adaptive weighted spatial pyramid structure(RSRC-ASP) is proposed. RSRC-ASP builds a spatial pyramid structure for sparse representation based classification with a self-adaptive weighting strategy for residuals' aggregation. In addition, three strategies, local-neighbourhood representation, local intra-class Bayesian residual criterion, and local auxiliary dictionary, are exploited to enhance the robustness of RSRC-ASP. Experiments on various data sets show that RSRC-ASP outperforms the classical sparse representation based classification methods especially for under-sampled face recognition problems. | Xiao MA Fandong ZHANG Yuelong LI Jufu FENG | 2018 | Science China(Information Sciences)2018，61，1： | 4 |
| 3 | High-resolution palmprint minutiae extraction based on Gabor feature显示文摘Extracting effective minutiae is difficult for high-resolution palmprint, because of the strong influence from principal lines, creases, and other noises. In this paper, a novel minutiae detection and reliability measurement method is proposed for high-resolution palmprint minutiae extraction. Firstly, we propose the Gabor Amplitude-Phase model for palmprint representation, which contains sufficient palmprint information and consists of the phase field and amplitude field. Because of the explicit meanings of minutiae in phase field,a minutiae descriptor is constructed to detect them directly. Also, to measure minutiae reliability and remove the unreliable ones, the Gabor Amplitude-Phase feature vector is designed. It can be used for describing the local area of a minutia redundantly. Then, the Adaboost algorithm is introduced in model training to select best features and corresponding weak classifiers for minutiae authenticity discriminant. Finally, the response value of weighted linear combination of selected weak classifiers is used for minutiae reliability measurement and unreliable ones removal. According to our analysis, the selected features are meaningful and useful for describing the minutiae area and measuring their reliability. Experimental results show that our proposed method is effective for minutiae extraction and can improve the matching performance. | FENG JuFu LIU ChongJin WANG Han SUN Bing | 2014 | Science China(Information Sciences)2014，57，11： | 4 |
| 4 | Boosting and margin theory显示文摘许多研究人员在理论研究了 AdaBoosts 好试验性的结果的解释。一些工作以边缘分发功能给归纳错误的上面的界限,当 Breiman 基于最小的边缘给了更锋利的归纳错误界限时。他也开发了 arcgv 算法最大化最小的边缘,然后使最小的边缘比 AdaBoost 大。然而,它的实验结果比 AdaBoost 甚至更坏。因此,最小的边缘界限是不实际的吗?这份报纸比最小的边缘跳的给一个新概念叫的平衡边缘(Emargin ) 并且证明一个新归纳错误用 Emargin 跳了,它总是好。另外,我们证明 Emargin 是归纳的好指示物。然后,我们进行证明 AdaBoost 的 Emargin 比 arc-gv 大,但是 Ada 增加的归纳错误通常更好的实验。 | Jufu FENG Liwei WANG Masashi SUGIYAMA Cheng YANG Zhi-Hua ZHOU Chicheng ZHANG | 2012 | Frontiers of Electrical and Electronic Engineering in China2012，7，1： | 4 |
| 5 | On the Euclidean Distance of Images显示文摘 | Wang Liwei Zhang Yan Feng Jufu | | 0，，08： | 1 |
| 6 | On the euclidean distance of images显示文摘 | Wang Liwei Zhang Yan Feng Jufu | 2000 | IEEE Transaction On Pattern Analysis And Machine Intelligence2000，27，8： | 1 |
| 7 | Subspace distance analysis with application to adaptive Bayesian face recognition显示文摘 | WANG Liwei WANG Xiao FENG Jufu | 2006 | Pattern Recognition2006，39，3： | 1 |
| 8 | Further results on the subspace distance显示文摘 | SUN Xichen WANG Liwei FENG Jufu | 2007 | Pattern Recognition2007，40，1： | 1 |
| 9 | On the Euclidean distance of images显示文摘 | Wang Liwei Zhang Yan Feng Jufu | 2005 | IEEE Transactions on Pattern Analysis and Machine Intelligence2005，27，8： | 1 |
| 10 | On the Euclidean distance of images 显示文摘 | WANG Liwei ZHANG Yan FENG Jufu | 2005 | IEEE Transactions on Pattern A- nalysis and Machine Intelligence2005，27，8： | 1 |
| 11 | On the euclidean distance of images显示文摘 | Wang Liwei Zhang Yan Feng Jufu | 2005 | IEEE Trans PAMI2005，27，8： | 1 |
| 12 | On the euclidean distance of images显示文摘 | Wang Liwei Zhang Yan Feng Jufu | 2005 | IEEE Transaction on Patten Analysis and Machine Intelligence2005，27，8： | 1 |
| 13 | Subspace Distance Analysis with Application to Adaptive Bayesian Algorithm for Face Recognition显示文摘 | Wang Liwei Wang Xiao Feng Jufu | 2006 | Pattern Recognitiion2006，39，3： | 1 |
| 14 | Further Results on the Subspace Distance显示文摘 | Sun Xichen Wang Liwei Feng Jufu | 2007 | Pattern Recognition2007，40，1： | 1 |
| 15 | On the Euclidean distance of images显示文摘 | Wang Liwei Zhang Yan Feng Jufu | 2005 | IEEE Transactions on Patter Analysis and Machine Intelligence2005，27，8： | 1 |
| 16 | On image matrix based feature extraction algorithm 显示文摘 | Wang Liwei Wang Xiao Feng Jufu | 2006 | IEEE Trans Systems Man and Cybernetics-part B: Cybernetics2006，36，1： | 1 |
| 17 | Intrapersonal subspace analysis with application to adaptive bayesian face recognition显示文摘 | Liwei Wang Xiao Wang Jufu Feng | 2005 | Pattern Recognition2005，38，： | 1 |
| 18 | On the Euclidean distance of images显示文摘 | Wang Liwei Zhang Yan Feng Jufu | 2005 | IEEE Transactions on Pattern Analysis and Machine Intelligence2005，27，8： | 1 |
| 19 | On the euclidean distance of images显示文摘 | Wang Liwei Zhang Yan Feng Jufu | 2005 | IEEE Transactions on Pattern Analysis and Machine Intelligence2005，27，8： | 1 |
| 20 | On image matrix based feature extraction Algorithms 显示文摘 | WANG Liwei WANG Xiao FENG Jufu | 2006 | IEEE Transactions on Systems Man and Cybernetics-Part B:Cybernetics2006，36，1： | 1 |
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