Abstract
Feature extraction from images, which are typical of high dimensionality, is crucial to the recognition performance. To explore the discriminative information while depressing the intra-class variations due to variable illumination and view conditions, we propose a factor analysis framework for separate “content” from “style,” identifying a familiar face seen under unfamiliar viewing conditions, classifying familiar poses presented in an unfamiliar face, estimating age across unfamiliar faces. The framework applies efficient algorithms derived from objective factor separating functions and space mapping functions, which can produce sufficiently expressive representations of feature extraction and dimensionality reduction. We report promising results on three different tasks in the high-dimensional image perceptual domains: face identification with two benchmark face databases, facial pose classification with a benchmark facial pose database, extrapolation of age to unseen facial image. Experimental results show that our approach produced higher classification performance when compared to classical LDA, WLDA, LPP, MFA, and DLA algorithms.
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References
Zhang Q, Zhang L, Yang Y et al (2014) Local patch discriminative metric learning for hyperspectral image feature extraction. IEEE Geosci Remote Sens Lett 11(3):612–616
Martinez AM, Kak AC (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23(2):228–233
Ye J, Janardan R, Park C, Park H (2004) An optimization criterion for generalized discriminant analysis on undersampled problems. IEEE Trans Pattern Anal Mach Intell 26(8):982–994
He X, Yan S, Hu Y, Niyogi P, Zhang H (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340
Tenenbaum J, Silva V, Langford J (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(22):2319–2323
Roweis S, Saul L (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(22):2323–2326
Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv Neural Inf Process Syst 14:585–591
Muller K, Mika S, Riitsch G, Tsuda K, Scho lkopf B (2001) An introduction to kernel-based learning algorithms. IEEE Trans Neural Netw 12:181–201
Yan S, Xu D, Yang Q, Zhang L, Tang X, Zhang H (2005) Discriminant analysis with tensor representation. Proc Int Conf Comput Vis Pattern Recognit 1:526–532
Yang J, Zhang D, Frangi A, Yang J (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137
Ye J (2004) Generalized low rank approximations of matrices. In: Proceedings of the international conference on machine learning, pp. 895–902
Ye J, Janardan R, Li Q (2005) Two-dimensional linear discriminant analysis. Adv Neural Inf Process Syst 17:1569–1576
Popa V, Nurminen J, Gabbouj M (2011) A study of bilinear models in voice conversion. J Signal Inf Process 2(2):125–139
Tan H, Cheng B, Feng J (2011) Tensor recovery via multi-linear augmented lagrange multiplier method, sixth international conference on image and graphics (ICIG), pp 141–146
Li Y, Gao Y, Erdogan H (2000) Weighted pairwise scatter to improve linear discriminant analysis. In: Proceedings of the ICSLP, pp 608–611
Loog M, Duin RPW, Haeb-Umbach R (2001) Multiclass linear dimension reduction by weighted pairwise Fisher criteria. TPAMI 23(7):762–766
H-S Lee, Chen B (2008) Linear discriminant feature extraction using weighted classification confusion information. In: INTERSPEECH 2008, 9th annual conference of the international speech communication association, Brisbane, Australia. Sept 2254–2257
Yan S, Xu D, Zhang B (2007) Graph embedding and extensions: a general framework for dimensionality reduction. TPAMI 29(1):40–51
Zhang T, Tao D, Li X (2009) Patch alignment for dimensionality reduction. IEEE Trans Knowl Data Eng 21(9):1299–1313
Bianco S (2015) Can linear data projection improve hyperspectral face recognition. Lect Notes Comput Sci 9016:161–170
Murphy-Chutorian E, Trivedi M (2009) Head pose estimation in computer vision: a survey. IEEE Trans Pattern Anal Mach Intell 31(4):607–626
Han H, Otto C, Jain AK (2013) Age estimation from face images: human vs. machine performance. In: International conference on biometrics, Madrid, Spain
AT & T Laboratories Cambridge. The ORL Database of Faces [OL]. http://www.cam-orl.Co.uk/facedatabase.html
Belhumeur PN, Hespanha JP, Kriengman DJ (1997) Eigenfaces versus fisherfaces: recognition using class specific linear projection. TPAMI 19(7):711–720
The CMU PIE database [OL] (2013) http://www.ri.cmu.edu/projects/project_418.html
The FGNET Aging Database[OL] (2013) http://www.prima.inrialpes.fr/FGnet
Liu SF, Lin Y (2005) Grey information: theory and practical applications. Springer, London
Acknowledgments
We want to thank the helpful comments and suggestions from Cheng-Lin Liu and Roger Lambert III. This work is supported partially by China Postdoctoral Science Foundation (No. 2015M582355), the doctor Scientific research start project from Hubei University of Science and Technology (No. BK1418), the National Natural Science Foundation of China (NSFC) (No. 61271256), the Team Plans Program of the Outstanding Young Science and Technology Innovation of Colleges and Universities in Hubei Province (No. T201513), and the Program of the Natural Science Foundation of Hubei Province (No. 2015CFB452).
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Liao, H., Chen, Y., Dai, W. et al. Tied factors analysis for high-dimensional image feature extraction and recognition application. Pattern Anal Applic 20, 587–600 (2017). https://doi.org/10.1007/s10044-016-0572-9
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DOI: https://doi.org/10.1007/s10044-016-0572-9