Advertisement

On Construction of Multi-class Binary Neural Network Using Fuzzy Inter-cluster Overlap for Face Recognition

  • Neha Bharill
  • Om Prakash Patel
  • Aruna Tiwari
  • Megha Mantri
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)

Abstract

In this paper, we propose a Novel Fuzzy-based Constructive Binary Neural Network (NF-CBNN) learning algorithm for multi-class classification. Our method draws a basic idea from Expand and Truncate Learning (ETL), which is a neural network learning algorithm. The proposed method works on the basis of unique core selection, and it guarantees to improve the classification performance by handling overlapping issues among data of various classes by using inter-cluster overlap. To demonstrate the efficacy of NF-CBNN, we tested it on the ORL face data set. The experimental results show that generalization accuracy achieved by NF-CBNN is much higher as compared to the BLTA classifier.

References

  1. 1.
    Cotter, N.E.: The stone-weierstrass theorem and its application to neural networks. IEEE Trans. Neural Netw./a Publ. IEEE Neural Netw. Counc. 1(4), 290–295 (1989)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Gray, D.L., Michel, A.N.: A training algorithm for binary feedforward neural networks. IEEE Trans. Neural Netw. 3(2), 176–194 (1992)CrossRefGoogle Scholar
  3. 3.
    Xu, Y., Chaudhari, N.: Application of binary neural networks for classification. In: 2003 International Conference on Machine Learning and Cybernetics, pp. 1343–1348. IEEE (2003)Google Scholar
  4. 4.
    Kim, J.H., Park, S.K.: The geometrical learning of binary neural networks. IEEE Trans. Neural Netw. 6(1), 237–247 (1995)CrossRefGoogle Scholar
  5. 5.
    Bharill, N., Tiwari, A.: Enhanced cluster validity index for the evaluation of optimal number of clusters for fuzzy c-means algorithm. In: IEEE World Congress on Computational Intelligence. International Conference on Fuzzy Systems(FUZZ-IEEE), pp. 1526–1533. IEEE, Beijing, China (2014)Google Scholar
  6. 6.
    Wang, S., Jiang, Y., Chung, F.L., Qian, P.: Feedforward kernel neural networks, generalized least learning machine, and its deep learning with application to image classification. Appl. Soft Comput. 37, 125–141 (2015)CrossRefGoogle Scholar
  7. 7.
    Pentland, A.: Looking at people: sensing for ubiquitous and wearable computing. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 107–119 (2000)CrossRefGoogle Scholar
  8. 8.
    Setnes, M., Babuska, R.: Fuzzy relational classifier trained by fuzzy clustering. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 29(5), 619–625 (1999)CrossRefGoogle Scholar
  9. 9.
    Kim, D.W., Lee, K.H., Lee, D.: On cluster validity index for estimation of the optimal number of fuzzy clusters. Pattern Recogn. 37(10), 2009–2025 (2004)CrossRefGoogle Scholar
  10. 10.
    Pigeon, S., Vandendorpe, L.: The m2vts multimodal face database (release 1.00). In: Audio-and Video-Based Biometric Person Authentication, pp. 403–409. springer (1997)Google Scholar
  11. 11.
    Angadi, S.A., Kagawade, V.C.: A robust face recognition approach through symbolic modeling of polar FFT features. Pattern Recogn. 71, 235–248 (2017)Google Scholar
  12. 12.
    Li, H., Suen, C.Y.: Robust face recognition based on dynamic rank representation. Pattern Recogn. 60, 13–24 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Neha Bharill
    • 1
  • Om Prakash Patel
    • 1
  • Aruna Tiwari
    • 1
  • Megha Mantri
    • 2
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology IndoreIndoreIndia
  2. 2.Department of Networth the Finance Club of Indian Institute of Management BangaloreBengaluruIndia

Personalised recommendations