스마트미디어저널 (Smart Media Journal)

  • 제6권3호
  • /
  • Pages.49-56
  • /
  • 2017
  • /
  • 2287-1322(pISSN)
  • /
  • 2288-9671(eISSN)
한국스마트미디어학회 (THE KOREAN INSTITUTE OF SMART MEDIA)
권호 표지

Human Face Tracking and Modeling using Active Appearance Model with Motion Estimation

  • Tran, Hong Tai (Department of Electronics and Computer Engineering, Chonnam National University) ;
  • Na, In Seop (Department of Electronics and Computer Engineering, Chonnam National University) ;
  • Kim, Young Chul (Department of Electronics and Computer Engineering, Chonnam National University) ;
  • Kim, Soo Hyung (Department of Electronics and Computer Engineering, Chonnam National University)
  • 투고 : 2017.07.17
  • 심사 : 2017.09.07
  • 발행 : 2017.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Images and Videos that include the human face contain a lot of information. Therefore, accurately extracting human face is a very important issue in the field of computer vision. However, in real life, human faces have various shapes and textures. To adapt to these variations, A model-based approach is one of the best ways in which unknown data can be represented by the model in which it is built. However, the model-based approach has its weaknesses when the motion between two frames is big, it can be either a sudden change of pose or moving with fast speed. In this paper, we propose an enhanced human face-tracking model. This approach included human face detection and motion estimation using Cascaded Convolutional Neural Networks, and continuous human face tracking and modeling correction steps using the Active Appearance Model. A proposed system detects human face in the first input frame and initializes the models. On later frames, Cascaded CNN face detection is used to estimate the target motion such as location or pose before applying the old model and fit new target.

키워드

참고문헌

  1. T. Cootes, G. Edwards, and C. Taylor, "Active Appearance Models," Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 484-498, 2001.
  2. I. Matthews, and S.Baker, "Active appearance models revisited," International journal of computer vision, vol. 60, no. 2, pp. 135-164, 2004. https://doi.org/10.1023/B:VISI.0000029666.37597.d3
  3. H. Li, Z. Lin, X. Shen, J. Brandt, and G. Hua, "A convolutional neural network cascade for face detection," IEEE Conference on Computer Vision and Pattern Recognition, pp. 5325-5334, 2015.
  4. S. Milborrow, J. Morkel, and F. Nicolls, "The MUCT Landmarked Face Database," Pattern Recognition Association of South Africa, 2010.
  5. P. Viola, and M. J. Jones, "Robust real-time face detection," International journal of computer vision, vol. 57, no. 2, pp. 137-154, 2004. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
  6. K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, "Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks," IEEE Signal Processing Letters, vol. 23, no. 10, pp. 1499-1503, 2016. https://doi.org/10.1109/LSP.2016.2603342
  7. E.N. Arcoverdo Neto, R.M. Duarte, R.M. Barreto, J.P. Magalhaes, C.M. Bastos, T.I. Ren and G.D.C. Cavalcanti, "Enhanced real-time head pose estimation system for mobile device," Integrated Computer Aided Engineering, vol. 21, no. 3, pp. 281-293, 2014.
  8. T. Cootes, C. Taylor, D. Cooper, and J. Graham, "Active shape models-their training and application," Computer Vision and Image Understanding, vol. 61, no. 1, pp. 38-59, 1995. https://doi.org/10.1006/cviu.1995.1004
  9. C. Zhang, and Z. Zhang, "Improving multiview face detection with multi-task deep convolutional neural networks," IEEE Winter Conference on Applications of Computer Vision, pp. 1036-1041, 2014.
  10. C. Bao, Y. Wu, H. Ling, and H. Yi, "Real Time Robust L1 Tracker Using Accelerated Proximal Gradient Approach", Conference on Computer Vision and Pattern Recognition, pp. 1830-1837, 2012
  11. http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html
  12. H. T. Tran, "Enhanced Model Based Human Face Tracking Method using CNN Cascade Face Detector",Masters Thesis, Chonnam National University, Korea, 2017.