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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 10/2020

03-04-2020 | Original Article

Exploring of alternative representations of facial images for face recognition

Authors: Yongbin Qin, Lilei Sun, Yong Xu

Published in: International Journal of Machine Learning and Cybernetics | Issue 10/2020

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Abstract

Description and classification of face images is a significant task of computer vision, machine learning and pattern recognition communities. In the past, researchers have made tremendous efforts in this task. Previous researchers always seek high-resolution face images for better image classification. However, with this paper, we present and demonstrate a new opinion that in some cases the use of alternative representations of facial images are very useful for face recognition and properly reducing the image resolution might be beneficial to better classification of face images. This may be attributed to the deformable property of faces and the fact that the proposed alternative representations can in some extent reduce the within-class difference of facial images. Also, the presented idea appear to be useful for helping people to improve face recognition techniques in real worlds.
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Literature
1.
Kan M, Wu J, Shan S, Chen X (2014) Domain adaptation for face recognition: targetize source domain bridged by common subspace. Int J Comput Vis 109(1–2):94–109MATHCrossRef
2.
Kim SG, Chang DY (2009) Underdetermined blind source separation based on subspace representation. IEEE Trans Signal Process 57(7):2604–2614MathSciNetMATHCrossRef
3.
Fan Z, Xu Y, Zuo W, Yang J, Tang J, Lai Z, Zhang D (2017) Modified principal component analysis: an integration of multiple similarity subspace models. IEEE Trans Neural Netw Learn Syst 25(8):1538–1552CrossRef
4.
Lai Z, Mo D, Wen J, Shen L, Wong WK (2018) Generalized robust regression for jointly sparse subspace learning. IEEE Trans Circuits Syst Video Technol 29(3):756–772CrossRef
5.
Li Z, Zhang Z, Qin J, Zhang Z, Shao L (2020) Discriminative fisher embedding dictionary learning algorithm for object recognition. IEEE Trans Neural Netw Learn Syst 31(3):786–800MathSciNetCrossRef
6.
Zhang Z, Yan S, Zhao M (2014) Similarity preserving low-rank representation for enhanced data representation and effective subspace learning. Neural Netw 53(5):81–94MATHCrossRef
7.
Xu Y, Zhong A, Yang J, Zhang D (2010) Lpp solution schemes for use with face recognition. Pattern Recognit 43(12):4165–4176MATHCrossRef
8.
Mi J, Zhu Q, Lu J (2019) Principal component analysis based on block-norm minimization. Appl Intell 49(6):2169–2177CrossRef
9.
Mi J, Zhang Y, Lai Z, Li W, Zhou L, Zhong F (2019) Principal component analysis based on nuclear norm minimization. Neural Netw 118:1–16MATHCrossRef
10.
Mansano A, Matsuoka JA, Afonso LCS, Papa JP, Faria F, Torres RDS (2012) Improving image classification through descriptor combination. In: Sibgrapi conference on graphics
11.
Yong X, Zhu X, Li Z, Liu G, Lu Y, Hong L (2013) Using the original and ’symmetrical face’ training samples to perform representation based two-step face recognition. Pattern Recognit 46(4):1151–1158CrossRef
12.
Shuai W, Jian C (2014) ‘Symmetrical face’ based improved lpp method for face recognition. Opt Int J Light Electron Opt 125(14):3530–3533CrossRef
13.
Xu Y, Li X, Yang J, Zhang D (2014) Integrate the original face image and its mirror image for face recognition. Neurocomputing 131(7):191–199CrossRef
14.
Wen X, Jie W (2016) Improved the minimum squared error algorithm for face recognition by integrating original face images and the mirror images. Opt Int J Light Electron Opt 127(2):883–889CrossRef
15.
Xu Y, Li X, Yang J, Lai Z, Zhang D (2014) Integrating conventional and inverse representation for face recognition. IEEE Trans Cybernet 44(10):1738–1746CrossRef
16.
Sanderson C, Paliwal KK (2003) Noise compensation in a person verification system using face and multiple speech features. Pattern Recognit 36(2):293–302CrossRef
17.
Wang Y, Min W, Yan C, Qi Z (2014) A novel virtual samples-based sparse representation method for face recognition. Opt Int J Light Electron Opt 125(15):3908–3912CrossRef
18.
Ryu YS, Oh SY (2002) Simple hybrid classifier for face recognition with adaptively generated virtual data. Pattern Recognit Lett 23(7):833–841MATHCrossRef
19.
Xu Y, Zhang B, Zhong Z (2015) Multiple representations and sparse representation for image classification. Pattern Recognit Lett 68:9–14CrossRef
20.
Mi J, Fu Q, Li W (2017) Adaptive class preserving representation for image classification, pp 2624–2632
21.
Lai Z, Xu Y, Yang J, Shen L, Zhang D (2016) Rotational invariant dimensionality reduction algorithms. IEEE Trans Cybern 47(11):3733–3746CrossRef
22.
Gao W, Shan S, Chai X, Fu X (2003) Virtual face image generation for illumination and pose insensitive face recognition. In: Proceedings of 2003 international conference on multimedia and expo, ICME’03 (Cat. No. 03TH8698), IEEE. vol 3, p 149
23.
Thian NPH, Marcel S, Bengio S (2003) Improving face authentication using virtual samples. In: Proceedings of 2003 IEEE international conference on acoustics, speech, and signal processing, 2003 (ICASSP’03), IEEE. vol 3, p 233
24.
Tang B, Luo S, Hua H (2004) High performance face recognition system by creating virtual sample. In: International conference on neural networks & signal processing
25.
Shi X, Yang Y, Guo Z, Lai Z (2014) Face recognition by sparse discriminant analysis via joint l2,1-norm minimization. Pattern Recognit 47(7):2447–2453CrossRef
26.
Wright J, Ma Y, Mairal J, Sapiro G, Huang TS, Yan S (2010) Sparse representation for computer vision and pattern recognition. Proc IEEE 98(6):1031–1044CrossRef
27.
Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227CrossRef
28.
Liu T, Mi J, Liu Y, Li C (2016) Robust face recognition via sparse boosting representation. Neurocomputing 214:944–957CrossRef
29.
30.
Xu Y, Zhang D, Yang J, Yang JY (2011) A two-phase test sample sparse representation method for use with face recognition. IEEE Trans Circuits Syst Video Technol 21(9):1255–1262MathSciNetCrossRef
31.
Zhang Z, Xu Y, Yang J, Li X, Zhang D (2017) A survey of sparse representation: algorithms and applications. IEEE Access 3:490–530CrossRef
32.
Lei Z, Meng Y, Feng X (2012) Sparse representation or collaborative representation: which helps face recognition? In: International conference on computer vision
33.
Xu Y, Fang X, Li X, Yang J, You J, Liu H, Teng S (2014) Data uncertainty in face recognition. IEEE Trans Cybern 44(10):1950–1961CrossRef
34.
Shi Q, Eriksson A, Hengel AVD, Shen C (2011) Is face recognition really a compressive sensing problem? In: Computer vision & pattern recognition
35.
Xu Y, Zhu Q, Fan Z, Zhang D, Mi J, Lai Z (2013) Using the idea of the sparse representation to perform coarse-to-fine face recognition. Inf Sci 238(7):138–148MathSciNetCrossRef
36.
Yu C, Jin Z, Chen H, Zhang Y, Yin X (2016) A fast and robust face recognition approach combining gabor learned dictionaries and collaborative representation. Int J Mach Learn Cybernet 7(1):47–52CrossRef
37.
Song Z, Cui K, Cheng G (2020) Image set face recognition based on extended low rank recovery and collaborative representation. Int J Mach Learn Cybern 11:71–80CrossRef
38.
Cai S, Lei Z, Zuo W, Feng X (2016) A probabilistic collaborative representation based approach for pattern classification. In: IEEE conference on computer vision & pattern recognition
39.
Zhu P, Zhang L, Hu Q, Shiu SCK (2012) Multi-scale patch based collaborative representation for face recognition with margin distribution optimization. In: European conference on computer vision
40.
Yang M, Zhang D, Wang S (2012) Relaxed collaborative representation for pattern classification. In: IEEE conference on computer vision & pattern recognition
41.
Xu Y, Zhu Q, Chen Y, Pan J-S et al (2013) An improvement to the nearest neighbor classifier and face recognition experiments. Int J Innov Comput Inf Control 9(2):543–554
42.
Kim SJ, Koh K, Lustig M, Boyd S, Gorinevsky D (2008) An interior-point method for large-scale l1-regularized least squares. IEEE J Sel Top Signal Process 1(4):606–617CrossRef
43.
Zouari M, Masmoudi AD, Masmoudi DS (2014) A non linear stretching image enhancement technique for microcalcification detection. In: 2014 1st international conference on advanced technologies for signal and image processing (ATSIP). IEEE, pp 193–197
44.
Dan C, Qi Z, Hui W, Feng S (2015) A novel color image contrast enhancement algorithm using saturation-lightness combined stretching in the hsl color space for amoled applications. In: IEEE international conference on solid-state & integrated circuit technology
45.
Nagaoka T, Nakamura A, Aizawa K, Kanazawa M, Kezuka T, Miura M, Usui M, Ohtsubo S, Sota T (2007) Application of decorrelation stretching method to hyperspectral fundus image processing. Proc SPIE 6426:526–533
46.
Karvelis PS, Fotiadis DI (2008) A region based decorrelation stretching method: application to multispectral chromosome image classification. In: IEEE international conference on image processing
47.
48.
49.
Metadata
Title
Exploring of alternative representations of facial images for face recognition
Authors
Yongbin Qin
Lilei Sun
Yong Xu
Publication date
03-04-2020
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 10/2020
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01116-4

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