Abstract
Collaborative representation is well known owing to its good performance in classification, especially classification on high-dimensional data. Collaborative representation does very well in classification problems of high-dimensional data, e.g., images classification. In this paper, we point out that conventional algorithm for collaborative representation does not well exert its potential. Our analysis shows that frequency-domain features of images provide good representations of objects and joint of frequency-domain features and space-domain features enables collaborative representation to perform very well in face recognition. The circular symmetry of the used frequency-domain features is exploited to design an efficient procedure for recognition of faces in the frequency domain. The setting procedure of the adaptive weight is also impressing because it can obtain reasonable weights for the two classifiers on two groups of data. It properly uses reliability of the data as weight of the corresponding classifier. The proposed joint collaborative representation algorithm achieves better result than conventional algorithm.
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References
Morel JM, Petro AB, Sbert C (2014) Screened poisson equation for image contrast enhancement. Image Process On Line 4:16–29
Huang K, Wu Z, Wang Q (2005) Image enhancement based on the statistics of visual representation. Image Vis Comput 23(1):51–57
Ke WM, Chen CR, Chiu CT (2011) BiTA/SWCE: image enhancement with bilateral tone adjustment and saliency weighted contrast enhancement. IEEE Trans Circuits Syst Video Technol 21(3):360–364
Schrijver DD, Sutter RD, Lambert P, Walle RVD (2005) Lossless image coding based on fractals. In: 7th IASTED International Conference on Signal and Image Processing, pp 51–56
Ha H-Y (2015) Integrating deep learning with correlation-based multimedia semantic concept detection. FIU Electronic Theses and Dissertations, 2268
Xu Y, Fang X, Wu J, Li X, Zhang D (2015) Discriminative transfer subspace learning via low-rank and sparse representation. IEEE Trans Image Process 25(2):1–1
Ngiam J, Khosla A, Kim M, Nam J, Lee H, Ng AY (2011) Multimodal deep learning. In: International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28–July 2011, pp 689–696
Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition. In: International Conference on Computer Vision, pp 471–478
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):1031–1044
Sun F, Tang J, Li H, Qi GJ, Huang TS (2014) Multi-label image categorization with sparse factor representation. IEEE Trans Image Process A Publ IEEE Signal Process Soc 23(3):1028–37
Yong X, Zhong Z, Jian Y, You J (2016) A new discriminative sparse representation method for robust face recognition via \(l_2\) regularization. IEEE Trans Neural Netw Learn Syst 1–10
Zhu P, Zuo W, Zhang L, Shiu CK (2013) Image set-based collaborative representation for face recognition. IEEE Trans Inf Forensics Secur 9(7):1120–1132
Yang X, Wang B, Li YR, He T (2015) Robust landmark-based image registration using \(l_1\) and \(l_2\) norm regularizations. In: IEEE International Conference on Bioinformatics and Biomedicine, pp 425–428
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):1255–1262
Zhang L, Shen Y, Li H, Lu J (2015) 3D palmprint identification using block-wise features and collaborative representation. IEEE Trans Pattern Anal Mach Intell 37(8):1730–1736
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):138–148
orel M, roubek F (2016) Fast convolutional sparse coding using matrix inversion lemma. Digit Signal Process 55:44–51
Xu N, Jiang A, Zhou L, Tang Y, Chen Y (2016) Image denoising via sparse coding using eigenvectors of graph Laplacian. Digit Signal Process 50:114–122
Mansano A, Matsuoka JA, Afonso LCS, Papa JP, Faria F, Torres RDS (2012) Improving image classification through descriptor combination. In: Sibgrapi Conference on Graphics, Patterns and Images, pp 324–329
Xu Y, Zhang B, Zhong Z (2015) Multiple representations and sparse representation for image classification. Pattern Recognit Lett 68:9–14
Hong X, Zhao G, Pietikainen M, Chen X (2014) Combining LBP difference and feature correlation for texture description. IEEE Trans Image Process A Publ IEEE Signal Process Soc 23(6):2557–68
Chen J, Shan S, He C, Zhao G, Pietikinen M, Chen X, Gao W (2010) WLD: a robust local image descriptor. IEEE Trans Pattern Anal Mach Intell 32(9):1705–1720
Long G, Kneip L, Li X., Zhang X (2015) Simplified mirror-based camera pose computation via rotation averaging. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Xu Y, Zhang Z, Lu G, Yang J (2016) Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification. Pattern Recognit 54(C):68–82
Zhang D, Xu Y, Zuo W (2016) Discriminative learning in biometrics. Springer, Berlin
Xu Y, Zhu Q (2013) A simple and fast representation-based face recognition method. Neural Comput Appl 22:1543. https://doi.org/10.1007/s00521-012-0833-5
Saha T, Rangwala H, Domeniconi C (2014) FLIP: active learning for relational network classification. In: Calders T, Esposito F, Meo R (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2014. Lecture Notes in Computer Science, vol 8726. Springer, Berlin
Liu X, Lingyun L, Shen Z, Kaixuan L (2018) A novel face recognition algorithm via weighted kernel sparse representation. Future Gener Comput Syst 28:653–663
Zhang W, Zhang Y, Ma L (2015) Multimodal learning for facial expression recognition. Pattern Recognit 48(10):3191–3202
Khaleghi B, Khamis A, Karray FO, Razavi SN (2013) Multisensor data fusion: a review of the state-of-the-art. Inf Fus 14(1):28–44
Chen B, Zhang W, Hu G, Yu L (2015) Networked fusion Kalman filtering with multiple uncertainties. IEEE Trans Aerosp Electron Syst 51(3):2232–2249
Yu L, Xia BY, Wang X, Lou XW (2015) Adaptive weighted fusion: a novel fusion approach for image classification. Neurocomputing 168:566–574
Xu Y, Li X, Yang J, Zhang D (2014) Integrate the original face image and its mirror image for face recognition. Neurocomputing 131(7):191–199
http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
Tian C, Zhang Q, Sun G et al (2018) FFT consolidated sparse and collaborative representation for image classification. Arab J Sci Eng 43:741. https://doi.org/10.1007/s13369-017-2696-7
Xu Y, Zhu X, Li Z (2013) Using the original and symmetrical face training samples to perform representation based two-step face recognition. Pattern Recognit 46(4):1151–1158
Yang J, Yang J, Frangi AF (2003) Combined fisherfaces framework. Image Vis Comput 21(12):1037–1044
Yong X, Zhang Z, Guangming L, Yang J (2016) Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification. Pattern Recognit 54:68–82
Wang Y, Tang Y, Li L (2015) Robust face recognition via minimum error entropy based atomic representation. IEEE Trans Image Process 24(12):5868–5878
Fuxing Z, Tao Z, Rui W (2017) Gbest-guided covariance matrix adaptation evolution strategy for large scale global optimization. In: Huang DS, Bevilacqua V, Premaratne P, Gupta P (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science, vol 10361. Springer, Cham
Chi H, Xia H, Tang X et al (2017) Supervised neighborhood regularized collaborative representation for face recognition. Multimed Tools Appl. https://doi.org/10.1007/s11042-017-4851-2
Acknowledgements
This work is supported by National Natural Science Foundation of Guangdong, China (No. 2016A030313023), and Shenzhen Science and Technology Program under Grant (No. JCYJ20160322114027138).
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Fan, X., Liu, K. & Yi, H. Joint collaborative representation algorithm for face recognition. J Supercomput 75, 2304–2314 (2019). https://doi.org/10.1007/s11227-018-2606-0
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DOI: https://doi.org/10.1007/s11227-018-2606-0