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