Abstract
Extreme learning machine (ELM) is one of the most important and efficient machine learning algorithms for pattern classification due to its fast learning speed. In this paper, we propose a new ensemble based ELM approach for cross-modality face matching. Different to traditional face recognition methods, the proposed approach integrates the voting-base extreme learning machine (V-ELM) with a novel feature learning based face descriptor. Firstly, the discriminant feature learning is proposed to learn the cross-modality feature representation. Then, we used common subspace learning based method to reduce the obtained cross-modality features. Finally, Voting ELM is utilized as the classifier to improve the recognition accuracy and to speed up the feature learning process. Experiments conducted on two different heterogeneous face recognition scenarios demonstrate the effectiveness of our proposed approach.
Similar content being viewed by others
References
Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns:application to face recognition. IEEE Trans Pattern Anal Mach Intell 11(12):2037–2041
Cao J, Lin Z, Huang G-B, Liu N (2012) Voting based extreme learning machine. Inf Sci 185(1):66–77
Cao J, Xiong L (2014) Protein sequence classification with improved extreme learning machine algorithms. BioMed Research International, p 2014
Chen J, Yi D, Yang J, Zhao G, Li S, Pietikainen M. (2009) Learning mapping for face synthesis from near infrared to visual light images. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 156–163
Fung G, Mangasarian OL (2001) Proximal support vector machine classifiers. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp 77–86
Gao X, Zhong J, Li J, Tian C (2005) Face sketch synthesis algorithm based on e-hmm and selective ensemble. IEEE Trans. Circuits Syst. Video Technol. 4:487–496
Hardoon DR, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: An overview with application to learning method. Neural Comput 16:2639–2664
Hotelling H (1936) Relations between two sets of variates. Biometrika 28:321–377
Huang G-B, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107–122
Huang G-B, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 42(2):513–529
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine theory and applications. Neurocomputing 70(1):489–501
Huang LK, Lu JW, Tan Y-P (2012) Learning modality-invariant features for heterogeneous face recognition. In: Proceedings of IEEE International Conference on Pattern Recognition, pp 1683– 1686
Huang XS, Lei Z, Fan MY, Wang X, Li SZ (2013) Regularized discriminative spectral regression method for heterogeneous face matching. IEEE Trans Image Process 22(1):353–362
HuiJuan L, An C, Zheng E, Yi L (2014) Dissimilarity based ensemble of extreme learning machine for gene expression data classification. Neurocomputing 128(0):22–30
Kasun LLC, Zhou H, Huang G-B, Vong CM (2013) Representational learning with extreme learning machine for big data. IEEE Intelligent Systems
Klare BF, Anil KJ (2013) Heterogeneous face recognition using kernel prototype similarities. IEEE Trans Pattern Anal Mach Intell 6:1410–1422
Klare BF, Jain AK (2013) Heterogeneous face recognition using kernel prototype similarities. IEEE Trans Pattern Anal Mach Intell 35(6):1410–1422
Klare BF, Li Z, Jain AK (2011) Matching forensic sketches to mug shot photos. IEEE Trans Pattern Anal Mach Intell 33(3):639–646
Lei Z, Li SZ (2009) Coupled spectral regressoin for matching heterogeneous faces. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 1123–1128
Lei Z, Liao SC, Jain AK, Li SZ (2012) Coupled discriminant analysis for heterogeneous face recognition. IEEE Trans Inf Forensics Secur 7(6):1707–1716
Lei Z, Pietikainen M, Li SZ (2014) Learning discriminant face descriptor. IEEE Trans Pattern Anal Mach Intell 36(2):289–302
Lei Z, Yi D, Li SZ (2012) Discriminant image filter learning for face recognition with local binary pattern like representation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 2512–2517
Li A, Shan S, Chen X, Gao W (2011) Face recognition based on non-corresponding region matching. In: Proceedings of IEEE International Conference on Computer Vision, pp 1060–1067
Li SZ, Lei Z, Ao M (2009) The hfb face database for heterogeneous face biometrics research. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp 1–8
Li Z, Gong D, Qiao Y, Tao D (2014) Common feature discriminant analysis for matching infrared face images to optical face images. IEEE Trans Image Process 23 (6):2436–2445
Liao S, Yi D, Lei Z, Qin R, Li S (2009) Heterogeneous face recognition from local structures of normalized appearance. In: Proceedings of International Conference on Biometrics, pp 209– 218
Lin D, Tang X (2006) Inter-modality face recognition. In: Proceedings of the European Conference on Computer Vision, pp 13–26
Liu N, Wang H (2010) Ensemble based extreme learning machine. IEEE Signal Process Lett 17(8):754–757
Liu Q, Tang X, Jin H, Lu H, Ma S (2005) A nonlinear approach for face sketch synthesis and recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 1005– 1010
Long X, Hongtao L, Peng Y, Li W (2014) Graph regularized discriminative non-negative matrix factorization for face recognition. Multimedia Tools and Applications 72(3):2679–2699
Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Mohammed AA, Minhas R, Jonathan Wu QM, Sid-Ahmed MA (2011) Human face recognition based on multidimensional pca and extreme learning machine. Pattern Recogn 44(10–11):2588– 2597
Phillips PJ, Flynn P, Scruggs T, Bowyer KW, Chang J, Hoffman K, Marques J, Min J, Worek W (2005) Overview of the face recognition grand challenge. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol 1, pp 947–954
Sharma A, Jacobs DW (2011) Bypassing synthesis,Pls for face recognition with pose, low-resolution and sketch. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 593– 600
Serre D (2002) Matrices: theory and applications. Springer
Sun Q, Zeng S, Liu Y, Heng PA, Xia DS (2005) A new method of feature fusion and its application in image recognition. Pattern Recogn 38(12):2437–2448
Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300
Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650
Tang X, Wang X (2004) Face sketch recognition. IEEE Trans Circuits Syst Video Technol 1:50–57
Wang X, Tang X (2009) Face photo-sketch synthesis and recognition. IEEE Trans Pattern Anal Mach Intell 11:1955–1967
Wold H (1975) Quantitative sociology: International perspectives on mathematical and statistical modeling (quantitative studies in social relations), vol 16. Academic press edn. Academic Press, London, pp 307–357
Yang D, Han F (2014) An improved ensemble of extreme learning machine based on attractive and repulsive particle swarm optimization. In: Intelligent Computing Theory, volume 8588 of Lecture Notes in Computer Science, pp 213–220
Yi D, Lei Z, Liao S, Li SZ (2014) Shared representation learning for heterogeneous face recognition. arXiv:1406.1247
Zhai J-h, Hong-yu X, Wang X-z (2012) Dynamic ensemble extreme learning machine based on sample entropy. Soft Comput 16(9):1493–1502
Zhang P, Zhixin Y (2015) Ensemble extreme learning machine based on a new self-adaptive adaboost.rt,. In: Proceedings of ELM-2014 Volume 1, of Proceedings in Adaptation, Learning and Optimization, vol 3, pp 237–244
Zong W, Huang G-B (2011) Face recognition based on extreme learning machine. Neurocomputing 74(16):2541–2551
Zong W, Zhou H, Huang G-B, Lin Z (2011) Face recognition based on kernelized extreme learning machine. In: Autonomous and Intelligent Systems, of Lecture Notes in Computer Science, vol 6752, pp 263–272
Zhu J-Y, Zheng W-S, Lai J-H, Li SZ (2014) Matching nir face to vis face using transduction. IEEE Trans Inf Forensics Secur 9(3):501–514
Acknowledgments
This work was supported by the fundamental research funds for the central universities (K14JB00230) and the National Natural Science Foundation of China (No. 61403024, 31201358, 61100141).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jin, Y., Cao, J., Wang, Y. et al. Ensemble based extreme learning machine for cross-modality face matching. Multimed Tools Appl 75, 11831–11846 (2016). https://doi.org/10.1007/s11042-015-2650-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-015-2650-1