The nearest-farthest subspace classification for face recognition

Mi, Jian-Xun; Huang, De-Shuang; Wang, Bing; Zhu, Xingjie

doi:10.1016/j.neucom.2013.01.003

Cited by 61 publications

(12 citation statements)

References 34 publications

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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%

“…Among these, LRC algorithm is the one which has been widely studied [1][2][3]. It makes the assumption that face images from a specific class are known to lie in a linear space.…”

Section: Introductionmentioning

confidence: 99%

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An Incremental Sparse Linear Regression Classification Algorithm for Face Recognition

Guang-chao¹,

Liu²,

Chen³

2017

dtcse

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Abstract. This paper mainly aims at improving the performance of Linear Regression Classification(LRC) when facing large scale training data. Based on LRC, we propose an Incremental Sparse Linear Regression Classification Algorithm (ISLRC) for face recognition. The ISLRC can easily obtain the representation coefficients without inverting a matrix and lower the negative effect of redundant samples or noise. These bring the benefits of faster training speed and higher recognition rate when the amount of the training data is large. Numerical experiments show that it provides on average a 46.23% reduction in training time and a 0.18% raise in recognition rate over the LRC at large scale face data.

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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%

See 1 more Smart Citation

An Incremental Sparse Linear Regression Classification Algorithm for Face Recognition

Guang-chao¹,

Liu²,

Chen³

2017

dtcse

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show abstract

“…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%

Differentiated representation and applications to face recognition

Wang

Chen²

2017

Optik

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“…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%