DOI QR코드

DOI QR Code

Design of Robust Face Recognition System Realized with the Aid of Automatic Pose Estimation-based Classification and Preprocessing Networks Structure

  • Kim, Eun-Hu (School of Electrical and Electronic Engineering, The University of Suwon) ;
  • Kim, Bong-Youn (School of Electrical and Electronic Engineering, The University of Suwon) ;
  • Oh, Sung-Kwun (School of Electrical and Electronic Engineering, The University of Suwon) ;
  • Kim, Jin-Yul (School of Electrical and Electronic Engineering, The University of Suwon)
  • Received : 2017.02.16
  • Accepted : 2017.07.17
  • Published : 2017.11.01

Abstract

In this study, we propose a robust face recognition system to pose variations based on automatic pose estimation. Radial basis function neural network is applied as one of the functional components of the overall face recognition system. The proposed system consists of preprocessing and recognition modules to provide a solution to pose variation and high-dimensional pattern recognition problems. In the preprocessing part, principal component analysis (PCA) and 2-dimensional 2-directional PCA ($(2D)^2$ PCA) are applied. These functional modules are useful in reducing dimensionality of the feature space. The proposed RBFNNs architecture consists of three functional modules such as condition, conclusion and inference phase realized in terms of fuzzy "if-then" rules. In the condition phase of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by the Fuzzy C-Means (FCM) algorithm. In conclusion phase of rules, the connections (weights) are realized through four types of polynomials such as constant, linear, quadratic and modified quadratic. The coefficients of the RBFNNs model are obtained by fuzzy inference method constituting the inference phase of fuzzy rules. The essential design parameters (such as the number of nodes, and fuzzification coefficient) of the networks are optimized with the aid of Particle Swarm Optimization (PSO). Experimental results completed on standard face database -Honda/UCSD, Cambridge Head pose, and IC&CI databases demonstrate the effectiveness and efficiency of face recognition system compared with other studies.

Keywords

References

  1. M. Balasubramanian, S. Palanivel and V. Ramalingam, "Real Time Face and Mouth Recognition Using Radial Basis Function Neural Networks," Expert Systems with Applications, vol. 36, pp. 6879-6888, April 2009. https://doi.org/10.1016/j.eswa.2008.08.001
  2. Wei Huang, Jinsong, Wang, Jiping Liao, A granular classifier by means of context-based similarity clustering," Journal of Electrical Engineering & Technology, vol. 11, no. 5, pp. 1383-1394, 2016. https://doi.org/10.5370/JEET.2016.11.5.1383
  3. Wen-zhun Huang, and Shan-wen Zhang, "A Novel Face Recognition Algorithm based on the Deep Convolution Neural Network and Key Points Detection Jointed Local Binary Pattern Methodology," Journal of Electrical Engineering & Technology, vol. 12, no. 1, pp. 363-372, 2017. https://doi.org/10.5370/JEET.2017.12.1.363
  4. Erik Murphy-Chutorian and Mohan Manubhai Trivedi "Head Pose Estimation in Computer Vision: A Survey," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 31, no. 4, pp. 607-626, April 2009. https://doi.org/10.1109/TPAMI.2008.106
  5. Meng Joo Er, Shiqian Wu, Juwei Lu and Hock Lye, "Face Recognition with Radial Basis Function (RBF) Neural Networks.", IEEE Trans. on Neural Networks, vol. 13, no. 3, pp. 697-710, May 2002. https://doi.org/10.1109/TNN.2002.1000134
  6. Daoqiang Zhang and Zhi-Hua Zhou, "Twodirectional Two-dimensional PCA for Efficient Face Representation and Recognition," Neurocomputing, vol. 69, no. 1-3, pp. 224-231, Dec. 2005. https://doi.org/10.1016/j.neucom.2005.06.004
  7. Minyoung Kim, Sanjiv Kumar, Vladimir Pavlovic and Henry Rowley, "Face Tracking and Recognition with Visual Constraints in Real-World Videos", Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference, June 2008.
  8. K. C. Lee, J. Ho, M. H. Yang and D. Kriegman, "Honda UCSD Video Database", Available: http://vision.ucsd.edu/content/honda-ucsdvideo-database, 2005, [Accessed: Oct 22, 2013]
  9. Sujith Srinivasan and Kim L. Boyer "Head Pose Estimation Using View Based Eigenspaces," in Proceedings of 16th International Conference on Pattern Recognition, Aug. 2002.
  10. Sung-Kwun Oh, Sung-Hoon Yoo and Witold Pedrycz, "Design of Face Recognition Algorithm Using PCALDA Combined for Hybrid Data Pre-processing and Polynomial-based RBF Neural Networks: Design and Its Application" Expert Systems with Applicants, vol. 40, no. 5, pp. 1451-1466, April 2013. https://doi.org/10.1016/j.eswa.2012.08.046
  11. Ergun Gumus, Niyazi Kilic, Ahmet Sertbas and Osman N. Ucan, "Evaluation of Face Recognition Technique Using PCA, Wavelets and SVM," Expert Systems with Applications, vol. 37, no. 9, pp. 6404- 6408, Sept. 2010. https://doi.org/10.1016/j.eswa.2010.02.079
  12. Sung-Hoon Yoo, Sung-Kwun Oh and Witold Pedrycz, "Optimized Face Recognition Algorithm Using Radial Basis Function Neural Networks and Its Practical Applications," Neural Networks, vol. 69, pp. 111-125, Sept. 2015. https://doi.org/10.1016/j.neunet.2015.05.001
  13. James Kennedy and Russell Eberhart, "Particle Swarm Optimization," in Proceedings of IEEE International Conference on Neural Networks, Dec. 1995.
  14. Peter N. Belhumeur, Joao P. Hespanha and Daivd J. Kriegman, "Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997. https://doi.org/10.1109/34.598228
  15. A. Hadid and M. Pietikainen, "From Still Image to Video-Based Face Recognition: An Experimental Analysis," in Proceedings of Sixth IEEE International Conference on Automatic Face and Gesture Recognition, May 2004.
  16. Osamu Yamaguchi, Kazuhiro Fukui and Ken-ichi Maeda, "Face Recognition Using Temporal Image Sequence," in Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, April 1998.
  17. Ruiping Wang, Shiguang Shan, Xilin Chen and Wen Gao, "Manifold-Manifold Distance with Application to Face Recognition based on Image Set" IEE Conference on Computer Vision and Pattern Recognition, June 2008.