2013
DOI: 10.1016/j.neucom.2013.01.003
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The nearest-farthest subspace classification for face recognition

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Cited by 61 publications
(12 citation statements)
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“…In order to illustrate the performance of ISLRC, we compare the performance of ISLRC, LRC FS and NFS [1,2]. For ISLRC, the parameter  is selected via 10-fold cross validation.…”
Section: Resultsmentioning
confidence: 99%
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“…In order to illustrate the performance of ISLRC, we compare the performance of ISLRC, LRC FS and NFS [1,2]. For ISLRC, the parameter  is selected via 10-fold cross validation.…”
Section: Resultsmentioning
confidence: 99%
“…As our new work is an improvement of LRC, this section describes the details of LRC [1][2]. Suppose there are N classes and each class has i m training images.…”
Section: Linear Regression Classification (Lrc)mentioning
confidence: 99%
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“…For instance, the dictionary learning method proposed in [16] uses a discriminative constraint. The original sample based method proposed by Mi et al [26] also designs a constraint condition for obtaining representation coefficients of different classes. However, because the method in [26] uses two separate procedures to obtain the solution, it appears that the designed constraint does not bring the desired performance.…”
Section: Introductionmentioning
confidence: 99%
“…However, according to [7], the authors argued that for the linear regressionbased classifiers, such as sparse-representation based classification (SRC), features extracted linearly, including the above mentioned ones, have little difference among them. In other words, even we use very simple feature extraction method as downsampling, it is highly possible that the recognition results are equal to the cases that very complicated linear features are used [8,9]. Therefore some studies suggested that for face recognition issue more attention should be paid to design high robust classifier since simple linear features are competent [10][11][12].…”
Section: Introductionmentioning
confidence: 99%