Abstract
In this paper, we present variations of one-directional two-dimensional principal component analysis (2DPCA) in hybrid pattern framework. Using hybrid pattern framework, we propose two novel methods, namely extended sub-image principal component analysis (ESIMPCA) and extended flexible principal component analysis (EFLPCA). The ESIMPCA operates on sub-image and full image at a time and captures the local and global variation of images. The dimensionality problem of ESIMPCA feature matrices is eliminated by further applying 2DPCA on two-dimensional ESIMPCA feature matrices to generate EFLPCA feature matrices. The summarization of variances, time and space complexities of the proposed methods and their relationship with some existing variations of one-directional 2DPCAs are addressed. The experiment is conducted on ORL and YALE face databases with different image resolutions. The experimental results, using EFLPCA, show superiority performance in terms of feature dimensionality, recognition accuracy and speed with reasonable space requirement over some existing variations of one-directional 2DPCA.
Similar content being viewed by others
References
Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2(4):433–459
Chatfield C (2018) Introduction to multivariate analysis. Routledge, New York
Chen S, Zhu Y (2004) Subpattern-based principle component analysis. Pattern Recognit 37(5):1081–1083
Choi Y, Ozawa S, Lee M (2014) Incremental two-dimensional kernel principal component analysis. Neurocomputing 134:280–288
Cui K, Gao Q, Zhang H, Gao X, Xie D (2015) Merging model-based two-dimensional principal component analysis. Neurocomputing 168:1198–1206
Ding C, Tao D (2016) A comprehensive survey on pose-invariant face recognition. ACM Trans Intell Syst Technol (TIST) 7(3):37
Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edn. Wiley, New York
Dunteman GH (1989) Principal components analysis. Sage, Thousand Oaks, p 69
Gao Q, Ma L, Liu Y, Gao X, Nie F (2017) Angle 2DPCA: a new formulation for 2DPCA. IEEE Trans Cybern 48(5):1672–1678
Gao Q, Xu S, Chen F, Ding C, Gao X, Li Y (2018) \(R_{1}\)-2-DPCA and face recognition. IEEE Trans Cybern 99:1–12
Gauch HG (1982) Noise reduction by eigenvector ordinations. Ecology 63(6):1643–1649
Gottumukkal R, Asari VK (2004) An improved face recognition technique based on modular PCA approach. Pattern Recognit Lett 25(4):429–436
Haiyang Z (2011) A comparison of PCA and 2DPCA in face recognition. In: Electrical power systems and computers. Springer, pp 445–449
Jj Hu, Gz Tan, Fg Luan, Libda A (2015) 2DPCA versus PCA for face recognition. J Central South Univ 22(5):1809–1816
Johnson RA, Wichern DW (eds) (1988) Applied multivariate statistical analysis. Prentice-Hall Inc, Upper Saddle River, NJ
Kim YG, Song YJ, Chang UD, Kim DW, Yun TS, Ahn JH (2008) Face recognition using a fusion method based on bidirectional 2DPCA. Appl Math Comput 205(2):601–607
Kumar KV, Negi A (2008a) Novel approaches to principal component analysis of image data based on feature partitioning framework. Pattern Recognit Lett 29(3):254–264
Kumar KV, Negi A (2008b) SubXPCA and a generalized feature partitioning approach to principal component analysis. Pattern Recognit 41(4):1398–1409
Li T, Li M, Gao Q, Xie D (2017) F-norm distance metric based robust 2DPCA and face recognition. Neural Netw 94:204–211
Mashhoori A, Jahromi MZ (2013) Block-wise two-directional 2DPCA with ensemble learning for face recognition. Neurocomputing 108:111–117
Negi A, Kadappa V (2010a) An investigation on recent advances in feature partitioning based principal component analysis methods. In: 2nd Vaagdevi international conference on information technology for real world problems (VCON). IEEE, pp 90–95
Negi A, Kadappa VK (2010b) SubXPCA versus PCA: a theoretical investigation. In: 20th international conference on pattern recognition (ICPR). IEEE, pp 4170–4173
Ozawa S, Takeuchi Y, Abe S (2010) A fast incremental kernel principal component analysis for online feature extraction. In: Pacific rim international conference on artificial intelligence. Springer, pp 487–497
Pizer SM, Johnston RE, Ericksen JP, Yankaskas BC, Muller KE (1990) Contrast-limited adaptive histogram equalization: speed and effectiveness. In: Proceedings of the 1st conference on visualization in biomedical computing. IEEE, pp 337–345
Sahoo TK, Banka H (2017) New hybrid PCA-based facial age estimation using inter-age group variation-based hierarchical classifier. Arab J Sci Eng 42(8):3337–3355
Sahoo TK, Banka H (2018) Multi-feature-based facial age estimation using an incomplete facial aging database. Arab J Sci Eng 43(12):8057–8078
Turhal ÜÇ, Duysak A (2015) Cross grouping strategy based 2DPCA method for face recognition. Appl Soft Comput 29:270–279
Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86
Wang L, Wang X, Zhang X, Feng J (2005) The equivalence of two-dimensional PCA to line-based PCA. Pattern Recognit Lett 26(1):57–60
Yang J, Zhang D, Frangi AF, Jy Yang (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137
Yu Q, Wang R, Yang X, Li BN, Yao M (2016) Diagonal principal component analysis with non-greedy \({\mathscr {L}}\)1-norm maximization for face recognition. Neurocomputing 171:57–62
Zhang D, Zhou ZH (2005) (2D) 2PCA: two-directional two-dimensional PCA for efficient face representation and recognition. Neurocomputing 69(1–3):224–231
Zhang D, Chen S, Zhou ZH (2006a) Recognizing face or object from a single image: linear vs. kernel methods on 2D patterns. In: Joint IAPR international workshops on statistical techniques in pattern recognition (SPR) and structural and syntactic pattern recognition (SSPR). Springer, pp 889–897
Zhang D, Zhou ZH, Chen S (2006b) Diagonal principal component analysis for face recognition. Pattern Recognit 39(1):140–142
Zhang L, Dong W, Zhang D, Shi G (2010) Two-stage image denoising by principal component analysis with local pixel grouping. Pattern Recognit 43(4):1531–1549
Zhu Q, Xu Y (2013) Multi-directional two-dimensional PCA with matching score level fusion for face recognition. Neural Comput Appl 23(1):169–174
Acknowledgements
We are thankful to Dr. Alok Ranjan Nayak of IIIT Bhubaneswar, India, for improving this manuscript.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sahoo, T.K., Banka, H. & Negi, A. Novel approaches to one-directional two-dimensional principal component analysis in hybrid pattern framework. Neural Comput & Applic 32, 4897–4918 (2020). https://doi.org/10.1007/s00521-018-3892-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-018-3892-4