[1] Hu Changhui, , Zhang Yang, et al. Centre symmetric quadruple pattern-basedillumination invariant measure [J]. Journal of Southeast University (English Edition), 2020, 36 (4): 407-413. [doi:10.3969/j.issn.1003-7985.2020.04.006]

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Centre symmetric quadruple pattern-basedillumination invariant measure()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

36

Issue:

2020 4

Page:

407-413

Research Field:

Computer Science and Engineering

Publishing date:

2020-12-20

Info

Title:

Centre symmetric quadruple pattern-basedillumination invariant measure

Author(s):

Hu Changhui¹;  2;  3;  Zhang Yang¹;  2;  Lu Xiaobo¹;  2;  Liu Pan³

¹School of Automation, Southeast University, Nanjing 210096, China
²Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education, Southeast University, Nanjing 210096, China
³School of Transportation, Southeast University, Nanjing 211189, China

Keywords:

centre symmetric quadruple pattern;  illumination invariant measure;  severe illumination variations;  single sample face recognition

PACS:

TP391.4

DOI:

10.3969/j.issn.1003-7985.2020.04.006

Abstract:

A centre symmetric quadruple pattern-based illumination invariant measure(CSQPIM)is proposed to tackle severe illumination variation face recognition. First, the subtraction of the pixel pairs of the centre symmetric quadruple pattern(CSQP)is defined as the CSQPIM unit in the logarithm face local region, which may be positive or negative. The CSQPIM model is obtained by combining the positive and negative CSQPIM units. Then, the CSQPIM model can be used to generate several CSQPIM images by controlling the proportions of positive and negative CSQPIM units. The single CSQPIM image with the saturation function can be used to develop the CSQPIM-face. Multi CSQPIM images employ the extended sparse representation classification(ESRC)as the classifier, which can create the CSQPIM image-based classification(CSQPIMC). Furthermore, the CSQPIM model is integrated with the pre-trained deep learning(PDL)model to construct the CSQPIM-PDL model. Finally, the experimental results on the Extended Yale B, CMU PIE and Driver face databases indicate that the proposed methods are efficient for tackling severe illumination variations.

References:

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Memo

Memo:

Biography: Hu Changhui(1983—), male, doctor, lecturer, 101101881@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.61802203), the Natural Science Foundation of Jiangsu Province(No.BK20180761), China Postdoctoral Science Foundation(No.2019M651653), Postdoctoral Research Funding Program of Jiangsu Province(No.2019K124).
Citation: Hu Changhui, Zhang Yang, Lu Xiaobo, et al.Centre symmetric quadruple pattern-based illumination invariant measure[J].Journal of Southeast University(English Edition), 2020, 36(4):407-413.DOI:10.3969/j.issn.1003-7985.2020.04.006.

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