Multimedia Tools and Applications

, Volume 78, Issue 6, pp 6969–6987 | Cite as

A novel construction method of convolutional neural network model based on data-driven

  • Guo-feng ZouEmail author
  • Gui-xia Fu
  • Ming-liang Gao
  • Jin Shen
  • Li-ju Yin
  • Xian-ye Ben


The convolutional neural network (CNN) is an excellent method for image recognition. However, there are some problems in the construction process of CNN model, such as network structure setting depends on experience knowledge, network parameters selection is difficult, and there is a lack of relevance between network model and training data. To overcome the shortcomings of CNN model construction theory, in this paper, we develop a new construction approach, named adaptive deep CNN network model based on data-driven. In our method, we first set up the initial CNN model in a simple way, and the initial model only contains one feature map in the convolution layer and pooling layer. And then, the network is adaptively constructed by using the idea of learning parameters and expanding network. In network expansion, the convergence rate of CNN model is used as evaluation index of global expansion, and some global branches are added to the network model. After global expansion, the CNN is controlled to local expansion according to the recognition rate of cross validation samples. The local network learning is stopped until the recognition rate reaches the expected value. Finally, the adaptive incremental learning of network structure is realized by expanding some new branches for new samples. Experimental results on two benchmark face databases, CMU-PIE face database and MIT-CBCL face database, demonstrate the effectiveness of the proposed method.


Deep convolutional neural network Adaptive model construction Data-driven Incremental learning Deep learning Image recognition 



This research is supported by Natural Science Foundation of Shandong Province of China (No.ZR2015FL029, ZR2016FL14); National Natural Science Foundation of China (No. 61601266); China Postdoctoral Science Foundation (No. 2017M612306); Key Research and Development Program of Shandong Province (No.2017GGX10125); The Shandong University of Technology and Zibo City Integration Development Project (No.2016ZBXC097, No. 2016ZBXC142).


  1. 1.
    Abdel-Hamid O, Mohamed AR, Jiang H, Deng L (2014) Convolutional Neural Networks for Speech Recognition[C]. IEEE/ACM Transactions on Audio, Speech, and Language Processing 22(10):1533–1545CrossRefGoogle Scholar
  2. 2.
    Arel I, Rose DC, Karnowski TP (2010) Deep machine learning: a new frontier in artificial intelligence research [J]. IEEE Comput Intell Mag 5(4):13–18CrossRefGoogle Scholar
  3. 3.
    Bengio Y, Delalleau O (2011) On the expressive power of deep architectures [C] Proc. of the 14th International Conference on Discovery Science:18-36Google Scholar
  4. 4.
    Dong Y, Wu Y (2015) Adaptive Cascade Deep Convolutional Neural Networks for face alignment[J]. Computer Standards & Interfaces 42:105–112CrossRefGoogle Scholar
  5. 5.
    Garcia C, Delakis M (2002) A neural architecture for fast and robust face detection[C], in: proc. 16th International Conference on Pattern Recognition, Canada 2: 44-47Google Scholar
  6. 6.
    Hamester D, Barros P, Wermter S (2015) Face Expression Recognition with a 2-Channel Convolutional Neural Network[C]. International Joint Conference on Neural Networks(IJCNN):1–8Google Scholar
  7. 7.
    He Y, Kavukcuoglu K, Wang Y, Szlam A, Qi Y (2014) Unsupervised feature learning by deep sparse coding, To be published on International Conference on Learning Representations:1-9Google Scholar
  8. 8.
    Hinton G, Salakhutdinov R (2006) Reducing the dimensionality of data with neural networks [J]. Science 5786(313):504–507MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hinton GE, Osindero S, Teh Y (2006) A fast learning algorithm for deep belief nets [J]. Neural Comput 18(7):1527–1554MathSciNetCrossRefGoogle Scholar
  10. 10.
    Huang Y, Huang K, Tao D, Tan T, Li X (2011) Enhanced biologically inspired model for object recognition [J]. IEEE Trans Syst Man Cybern B Cybern 41(6):1668–1680CrossRefGoogle Scholar
  11. 11.
    Huang K-Q, Ren W-Q, Tan T-N (2014) A Review on Image Object Classification and Detection[J]. Chinese journal of computer 37(6):1225–1240Google Scholar
  12. 12.
    Kang L, Kumar J, Ye P, Li Y, Doermann D (2014) Convolutional Neural Networks for Document Image Classification[C]. International Conference on Pattern Recognition(ICPR):3168–3172Google Scholar
  13. 13.
    Kang L, Ye P, Li Y, Doermann D (2014) Convolutional Neural Networks for No-Reference Image Quality Assessment[J]. Computer Vision & Pattern Recognition(CVPR):1733–1740Google Scholar
  14. 14.
    Kim LW, Asaad S, Linsker R (2014) A fully pipelined FPGA architecture of a factored restricted boltzmann machine artificial neural network [J]. ACM Transactions on Reconfigurable Technology and Systems 7(1):104–104CrossRefGoogle Scholar
  15. 15.
    Krizhevsky A, Sutskever I, Hinton G (2012) ImageNet classification with deep convolutional neural networks [C] Proc. of the Advances in Neural Information Processing Systems (NPIS):1106-1114Google Scholar
  16. 16.
    Lawrence S, Giles CL, Tsoi AC, Back AD (1997) Face recognition: a convolutional neural-network approach[J]. IEEE Trans Neural Netw 8(1):98–113CrossRefGoogle Scholar
  17. 17.
    LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Back propagation applied to hand written zip code recognition [J]. Neural Comput 1(4):541–551CrossRefGoogle Scholar
  18. 18.
    Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-Based Learning Applied to Document Recognition[J]. Proc IEEE 86(11):2278–2324CrossRefGoogle Scholar
  19. 19.
    Li H, Lin Z, Shen X, Brandt J, Hua G (2015) A Convolutional Neural Network Cascade for Face Detection[C], The IEEE Conference on Computer Vision and Pattern Recognition (CVPR):5325-5334.Google Scholar
  20. 20.
    Lian Z, Jing X, Sun S et al (2016) Multi-Scale convolutional neural network model with multilayer maxout networks[J]. Journal of Beijing University of Posts and Telecommunications 39(5):1–5Google Scholar
  21. 21.
    Ouyang WL, Wang XG, Zeng XY et al (2015) DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR):2403–2412Google Scholar
  22. 22.
    Pattabhi Ramaiah N, Ijjina EP, Mohan CK (2015) Illumination invariant face recognition using convolutional neural network[C], 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems:1-4.Google Scholar
  23. 23.
    Pinheiro P, Collobert R (2014) Recurrent convolutional neural networks for scene labeling [C] Proc. of The 31st International Conference on Machine Learning:82-90Google Scholar
  24. 24.
    Rui T, Zou J, Zhou Y et al (2017) Convolutional neural network feature maps selection based on LDA[J]. Multimedia Tools & Applications 6:1–15Google Scholar
  25. 25.
    Sun Y, Wang X, Tang X (2013) Hybrid deep learning for face verification [C] Proc. of IEEE International Conference on Computer Vision (ICCV 2013):1489-1496Google Scholar
  26. 26.
    Wolfshaar JVD, Karaaba MF, Wiering MA (2015) Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition[C]. Proceedings - 2015 IEEE Symposium Series on Computational Intelligence:188-195.Google Scholar
  27. 27.
    Xiong C, Zhao X, Tang D, Jayashree K (2015) Conditional Convolutional Neural Network for Modality-aware Face Recognition[C]. IEEE International Conference on Computer Vision(CVPR):3667–3675Google Scholar
  28. 28.
    Xu C, Yang J, Lai H et al. (2017) UP-CNN: Un-pooling augmented convolutional neural network[J], Pattern Recogn Lett:1-7Google Scholar
  29. 29.
    Yu K, Jia L, Chen Y, Xu W (2013) Deep Learning: Yesterday, Today and Tomorrow[J]. Journal of computer research and development 50(9):1799–1804Google Scholar
  30. 30.
    Zhang S, Yang H, Yin Z (2015) Multiple deep convolutional neural networks averaging for face alignment[J]. Journal of Electronic Imaging 24(3):1–13CrossRefGoogle Scholar
  31. 31.
    Zhang Y, Zhao D, Sun J, Zou G, Li W (2016) Adaptive Convolutional Neural Network and Its Application in Face Recognition[J]. Neural Process Lett 43(2):389–399CrossRefGoogle Scholar
  32. 32.
    Zou J, Wu Q, Tan Y, Wu F (2015) Analysis Range of Coefficients in Learning Rate Methods of Convolution Neural Network[J]. International Symposium on Distributed Computing & Applications for Business Engineering & Science(DCABES):513–517Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Guo-feng Zou
    • 1
    Email author
  • Gui-xia Fu
    • 1
  • Ming-liang Gao
    • 1
  • Jin Shen
    • 1
  • Li-ju Yin
    • 1
  • Xian-ye Ben
    • 2
  1. 1.College of Electrical and Electronic EngineeringShandong University of TechnologyZiboChina
  2. 2.School of Information Science and EngineeringShandong UniversityJinanChina

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