Multimanifold analysis with adaptive neighborhood in DCT domain for face recognition using single sample per person

Nabipour, Mehrasa; Aghagolzadeh, Ali; Motameni, Homayun

doi:10.1109/iraniancee.2014.6999668

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Figure 5 depicts the recognition rate while using a different number of redundant samples. Figure 4 recognition rate of the presented method and other algorithms Figure 5 recognition rate under different number of redundant sample Some researches about single-sample face recognition were also done in literatures [8][9][10][11][12]. It can be seen from Figure 6 that the presented method increases 7.63% than the original SRC on FERET face database.…”

Section: Feretmentioning

confidence: 94%

Single-Sample Face Recognition Based on WSSRC and Expanding Sample

2015

Lecture Notes in Computer Science

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This paper proposes a kind of face recognition method with one training image per person, which is based on compressed sensing. There are two methods-nonlinear dimensionality reduction by locally linear embedding and sparse coefficients, by which redundant samples can generate. These new samples with multi-expressive and multi-gesture can be treated as training samples. Finally a super sparse random projection and weighted optimization have been used to improve the SRC (weighted super sparse representation classification, WSSRC). The improved SRC algorithm can be applied to face recognition. Experiments on the well-known ORL face database and FERET face database show that the presented method is respectively about 15.53% and 7.67%, more accurate than original SRC method in the context of single sample face recognition problem. In addition, extensive experimentation reported in this paper suggests that the presented method achieve higher recognition rate than RSRC, SSRC DMMA, and DCT-based DMMA.

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Section: Feretmentioning

confidence: 94%

Single-Sample Face Recognition Based on WSSRC and Expanding Sample

2015

Lecture Notes in Computer Science

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Single Sample Face Recognition in the Last Decade: A Survey

Kumar

Garg

2019

Int. J. Patt. Recogn. Artif. Intell.

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Single sample face recognition (SSFR) is a challenging research problem in which only one face image per person is available for training. Moreover, the face image may have different pose, expression, illumination, occlusion etc. rendering this problem more complex. Several methods have been suggested by various researchers in literature to solve SSFR. Here, we provide a comprehensive review of the methods proposed in the last decade for solving SSFR problem and introduce a novel taxonomy for the same. We divide SSFR methods broadly into five categories viz. (i) feature based, (ii) virtual sample generation based, (iii) generic database based, (iv) Hybrid and (v) other methods. We have also briefly reviewed the face databases used for evaluating single sample face recognition methods. Furthermore, the performance of the methods has been analyzed in terms of classification accuracy as given in literature. At last, we also suggest some future direction to the researchers and practitioners working in this fascinating research area.

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Multimanifold analysis with adaptive neighborhood in DCT domain for face recognition using single sample per person

Cited by 2 publications

References 24 publications

Single-Sample Face Recognition Based on WSSRC and Expanding Sample

Single-Sample Face Recognition Based on WSSRC and Expanding Sample

Single Sample Face Recognition in the Last Decade: A Survey

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