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Face Classification Using Cascade Facial Detection and Convolutional Neural Network

Cascade 안면 검출기와 컨볼루셔널 신경망을 이용한 얼굴 분류

  • Received : 2016.01.07
  • Accepted : 2016.02.11
  • Published : 2016.02.25

Abstract

Nowadays, there are many research for recognizing face of people using the machine vision. the machine vision is classification and analysis technology using machine that has sight such as human eyes. In this paper, we propose algorithm for classifying human face using this machine vision system. This algorithm consist of Convolutional Neural Network and cascade face detector. And using this algorithm, we classified the face of subjects. For training the face classification algorithm, 2,000, 3,000, and 4,000 images of each subject are used. Training iteration of Convolutional Neural Network had 10 and 20. Then we classified the images. In this paper, about 6,000 images was classified for effectiveness. And we implement the system that can classify the face of subjects in realtime using USB camera.

Keywords

Machine vision;Convolutional Neural Network;Cascade face detector;Human-robot interaction

References

  1. K. E. Ko and K. B. Sim, "A Study on Human-Robot Interface based on Imitative Learning using Computational Model of Mirror Neuron System", Journal of Korean Institute of Intelligent Systems, vol. 23, no. 6, pp. 565-570, 2013 https://doi.org/10.5391/JKIIS.2013.23.6.565
  2. A. Kumar, "Computer-Vision-Based Fabric Defect Detection: A Survey", IEEE Transactions on Industrial Electronics, vol. 55, pp. 348-363, 2015
  3. K. M. Jeong and J. H. Kim, "Face classification and analysis based on geometrical feature of face", Journal of the Korea Institute of Information and Communication Engineering, vol. 16, pp. 1495-1504, 2012 https://doi.org/10.6109/jkiice.2012.16.7.1495
  4. Y. Sun, X. Wang and X. Tang, "Deep Convolutional Network Cascade for Facial Point Detection", 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3476-3483, 2013.
  5. S. Berretti, B. B. Amor, M. Daoudi and A. Del Bimbo, "3D facial expression recognition using SIFT descriptors of automatically detected keypoints", The Visual Computer, vol. 27, pp. 1021-1036, 2011. https://doi.org/10.1007/s00371-011-0611-x
  6. J. Wang, R. Xiong and J Chu, "Facial feature points detecting based on Gaussian Mixture Models", Pattern recognition letters, vol. 53, pp. 62-68, 2015. https://doi.org/10.1016/j.patrec.2014.11.004
  7. E. Owusu, Y. Zhan and Q. R. Mao, "An SVM-AdaBoost facial expression recognition system", Applied Intelligence, vol. 40, pp. 536-545, Apr 2014. https://doi.org/10.1007/s10489-013-0478-9
  8. H. J. Go, H. B. Kim, D. H. Yang, J. H. Park and M. G. Chun, "Face Recognition Under Ubiquitous Environments", Journal of Korean Institute of Intelligent Systems, vol. 14, no. 4, pp. 431-437, 2004
  9. J. Y. Kim and Y. S. Kim, "Face Tracking and Recognition in Video with PCA-based Pose-Classification and $(2D)^2PCA$ recognition algorithm", Journal of Korean Institute of Intelligent Systems, vol. 23, no. 5, pp. 423-430, 2013 https://doi.org/10.5391/JKIIS.2013.23.5.423
  10. S. I. Choi, C. H. Kim and C. H. Choi, "Shadow Compensation in 2D Images for Face Recognition", Pattern Recognition, vol. 40, no. 7, pp. 2118-2125, 2007. https://doi.org/10.1016/j.patcog.2006.11.020
  11. S. I. Choi, "Construction of Composite Feature Vector Based on Discriminant Analysis for Face Recognition", Journal of Korea Multimedia Society, vol. 18, no. 7, pp. 834-842, 2015. https://doi.org/10.9717/kmms.2015.18.7.834
  12. C. M. Ma, S. H. Yoo and S. K. Oh, "Design of Face Recognition Algorithm based Optimized pRBFNNs Using Three-dimensional Scanner", Journal of Korea Institute of Intelligent Systems, vol. 22, no.6, pp. 748-753, 2012. https://doi.org/10.5391/JKIIS.2012.22.6.748
  13. P. Viola and M. Jones "Rapid object detection using a boosted cascade of simple features", Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511-518, 2001.
  14. A Jain, J Bharti and MK Gupta, "Improvements in OpenCV's Viola Jones Algorithm in Face Detection-Tilted Face Detection", International journal of Signal and Image Processing, vol. 5, pp. 21-28, 2014
  15. W. Wang, J. Yang, J. Xiao, S. Li and D. Zhou, "Face Recognition Based on Deep Learning", Human Centered Computing, vol. 8944, pp. 812-820, 2015.
  16. Y. Bengio, "Learning deep architectures for AI", Foundations and Trends(R) in Machine Learning, vol. 2, pp. 1-127, Jan 2009. https://doi.org/10.1561/2200000006
  17. R. Hecht-Nielsen, "Theory of the backpropagation neural network", International Joint Conference on Neural Networks, vol. 1, pp. 593-605, 1989
  18. Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-based learning applied to document recognition", Proceedings of the IEEE, vol. 86, no 11, pp. 2278-2324, 2015.
  19. D. M. Kwak, S. W. Park and H. N. Lee, Machine Learning to Deep Learning, PubPle, Seoul, 2015.
  20. J. H. Yu, S. M. Park, K. E. Ko and K. B. Sim, "Face classification using cascade facial detection and convolutional neural network", Proceeding of Korean Institute of Intelligent Systems Fall Conference, vol. 25, no. 2, pp. 157-159, 2015

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Acknowledgement

Supported by : 한국연구재단