2篇
您的检索式：关键字=sparse recognition
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Robust Face Recognition via Low-rank Sparse Representation-based Classification显示文摘Face recognition has attracted great interest due to its importance in many real-world applications. In this paper,we present a novel low-rank sparse representation-based classification(LRSRC) method for robust face recognition. Given a set of test samples, LRSRC seeks the lowest-rank and sparsest representation matrix over all training samples. Since low-rank model can reveal the subspace structures of data while sparsity helps to recognize the data class, the obtained test sample representations are both representative and discriminative. Using the representation vector of a test sample, LRSRC classifies the test sample into the class which generates minimal reconstruction error. Experimental results on several face image databases show the effectiveness and robustness of LRSRC in face image recognition. | Hai-Shun Du Qing-Pu Hu Dian-Feng Qiao Ioannis Pitas | 2015 | International Journal of Automation and computing2015，12，6： | 2 |
| 2 | Pre-detection and dual-dictionary sparse representation based face recognition algorithm in non-sufficient training samples显示文摘Face recognition based on few training samples is a challenging task. In daily applications, sufficient training samples may not be obtained and most of the gained training samples are in various illuminations and poses. Non-sufficient training samples could not effectively express various facial conditions, so the improvement of the face recognition rate under the non-sufficient training samples condition becomes a laborious mission. In our work, the facial pose pre-recognition(FPPR) model and the dualdictionary sparse representation classification(DD-SRC) are proposed for face recognition. The FPPR model is based on the facial geometric characteristic and machine learning, dividing a testing sample into full-face and profile. Different poses in a single dictionary are influenced by each other, which leads to a low face recognition rate. The DD-SRC contains two dictionaries, full-face dictionary and profile dictionary, and is able to reduce the interference. After FPPR, the sample is processed by the DD-SRC to find the most similar one in training samples. The experimental results show the performance of the proposed algorithm on olivetti research laboratory(ORL) and face recognition technology(FERET) databases, and also reflect comparisons with SRC, linear regression classification(LRC), and two-phase test sample sparse representation(TPTSSR). | ZHAO Jian ZHANG Chao ZHANG Shunli LU Tingting SU Weiwen JIA Jian | 2018 | Journal of Systems Engineering and Electronics2018，29，1： | 2 |
您所在的机构暂未开通此部分的功能，请联系管理员