Multispectral palmprint recognition using multiclass projection extreme learning machine and digital shearlet transform

详细信息    查看全文

• 作者：Xuebin Xu ; Longbin Lu ; Xinman Zhang ; Huimin Lu…
• 关键词：Extreme learning machine ; Multispectral palmprint ; Digital shearlet transform ; Image fusion
• 刊名：Neural Computing & Applications
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：27
• 期：1
• 页码：143-153
• 全文大小：1,681 KB
• 参考文献：1.Zhang D, Guo Z, Lu G, Zhang L, Zuo W (2010) An online system of multispectral palmprint verification. IEEE Trans Instrum Measurement 59(2):480–490CrossRef
  2.Xu X, Guo Z, Song C, Li Y (2012) Multispectral palmprint recognition using a quaternion matrix. Sensors 12:4633–4647CrossRef
  3.Rowe RK, Uludag U, Demirkus M, Parthasaradhi S, Jain AK (2007) A multispectral whole-hand biometric authentication system. In: Proceedings of the biometric symposium, biometric consortium conference. Baltimore, MD, pp 1–6
  4.Han D, Guo Z, Zhang D (2008) Multispectral palmprint recognition using wavelet-based image fusion. Int Conf Signal Process 2008:2074–2077
  5.Hao Y, Sun Z, Tan T, Ren C (2008) Multispectral palm image fusion for accurate contact-free palmprint recognition. ICIP 2008:281–284
  6.Xu Y, Zhu Q (2011) PCA-based Multispectral band compression and multispectral palmprint recognition. In: 2011 international conference on hand-based biometrics (ICHB), pp 1–4
  7.Meraoumia A, Chitroub S, Bouridane A (2011) Fusion of multispectral palmprint images for automatic person identification. In: Proceedings of the Saudi international electronics, communications and photonics conference (SIECPC), pp 1–6
  8.Huang G-B, Wang D (2011) Advances in extreme learning machines (ELM2010). Neurocomputing 74(16):2411–2412CrossRef
  9.Huang G-B, Zhu Qin-Yu, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501CrossRef
  10.Huang G-B, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71:3460–3468 CrossRef
  11.Huang G, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70(16–18):3056–3062CrossRef
  12.Mohammed AA, Minhas R, Jonathan Wu QM (2011) Human face recognition based on multidimensional PCA and extreme learning machine. Pattern Recogn 44(10–11):2588–2597MATH CrossRef
  13.Deng W, Zheng Q, Lian S (2010) Ordinal extreme learning machine. Neurocomputing 74(3):447–456CrossRef
  14.Cao J, Lin Z, Huang G-B (2012) Voting based extreme learning machine. Inf Sci 185(1):66–77CrossRef MathSciNet
  15.Deng W, Zheng Q, Lian S, Chen L (2012) Projection vector machine: one-stage learning algorithm from high-dimension small-sample data. In: The 2010 international joint conference on neural networks (IJCNN), pp 1–8
  16.Zong W, Huang G-B (2011) Face recognition based on extreme learning machine. Neurocomputing 74(16):2541–2551CrossRef
  17.Wang Y, Cao F, Yuan Y (2011) A study on effectiveness of extreme learning machine. Neurocomputing 74(16):2483–2490CrossRef
  18.Donoho DL, Kutyniok G, Shahram M, Zhuang X. A rational design of digital shearlet transform. http://​www.​home.​uni-osnabrueck.​de/​kutyniok/​papers/​ShearLab_​SampTA.​pdf
  19.Easley G, Labate D, Lim WQ (2006) Sparse directional image representations using the discrete shearlet transform. In: Proceedings of conference on signals, systems and computers, ACSSC, pp 974–978
  20.Lim W-Q (2010) The discrete shearlet transform: a new directional transform and compactly supported shearlet frames. IEEE Trans Image Process 19(5):1166–1180CrossRef MathSciNet
  21.Pennec E, Mallat S (2005) Sparse geometric image representation with bandelets. IEEE Trans Image Process 14(4):423–438CrossRef MathSciNet
  22.Kutyniok G, Lim W-Q (2011) Compactly supported shearlets are optimally sparse. J Approx Theory 163(11):1564–1589MATH CrossRef MathSciNet
  23.Zhang D, Kong W-K, You J, Wong M (2003) Online palmprint identification. IEEE Trans Pattern Anal Mach Intell 25(9):1041–1049CrossRef
  24.Jia W, Huang DS, Zhang D (2008) Palmprint verification based on robust line orientation code. Pattern Recogn 41(5):1504–1513MATH CrossRef
  25.Yao YF, Jing XY, Wong HS (2007) Face and palmprint feature level fusion for single sample biometrics recognition. Neurocomputing 70(7–9):1582–1588 CrossRef
  26.Vapnic V (1998) Statistical learning theory. Wiley, New York
  27.Lopez J, Dorronsoro JR (2012) Simple proof of convergence of the SMO algorithm for different SVM variants. IEEE Trans Neural Netw Learn Syst 23(7):1142–1147CrossRef
  28.Kuan T-W, Wang J-F, Wang J-C (2012) VLSI design of an SVM learning core on sequential minimal optimization algorithm. IEEE Trans Very Large Scale Integr Syst 20(4):673–683CrossRef
  29.Cai F, Cherkassky V (2012) Generalized SMO algorithm for SVM-based multitask learning. IEEE Trans Neural Netw Learn Syst 23(6):997–1003CrossRef
  30.Chasanis V, Likas A, Galatsanos N (2009) Simultaneous detection of abrupt cuts and dissolves in videos using support vector machines. Pattern Recogn Lett 30(1):55–65CrossRef
  31.He X, Yan S, Hu Y et al (2005) Face recognition using Laplacian faces. IEEE Trans Pattern Anal Mach Intell 27:328–340CrossRef
  
• 作者单位：Xuebin Xu (1)
  Longbin Lu (1)
  Xinman Zhang (1)
  Huimin Lu (2)
  Wanyu Deng (1)
  
  1. School of Electronics and Information Engineering, Xi’an Jiaotong University, 28 Xian’ning West Road, Xi’an, 710049, China
  2. School of Software Engineering, Changchun University of Technology, Changchun, 130012, China
  
• 刊物类别：Computer Science
• 刊物主题：Simulation and Modeling
  
• 出版者：Springer London
• ISSN：1433-3058

文摘

A novel multispectral palmprint recognition method is proposed based on multiclass projection extreme learning machine (MPELM) and digital shearlet transform. Extreme learning machine (ELM) is a novel and efficient learning machine based on the generalized single-hidden-layer feedforward networks, which performs well in classification applications. Many researchers’ experimental results have shown the superiority of ELM with classical algorithm: support vector machine (SVM). To further improve the performance of multispectral palmprint recognition method, we propose a novel method based on MPELM in this paper. Firstly, all palmprint images are preprocessed by David Zhang’s method. Then, we use image fusion method based on fast digital shearlet transform to fuse the multispectral palmprint images. At last, we use the proposed MPELM classifier to determine the final multispectral palmprint classification. The experimental results demonstrate the superiority of multispectral fusion to each single spectrum, and the proposed MPELM-based method outperforms the SVM-based and ELM-based methods. The proposed method is also suitable for other biometric applications and gets to be work well.