COMBINED APPROACH FOR FACE FRONTAL VIEW ESTIMATION FOR REAL TIME FACES DETECTION IN MULTICAMERA SYSTEM

Authors

  • Denis V. Lamovsky
  • Rauf Kh. Sadykhov
  • Vadim A. Kharlanov
  • Alexandr S. Kirienko

DOI:

https://doi.org/10.47839/ijc.10.3.750

Keywords:

Face detection, tracking, and frontal view estimation.

Abstract

This paper presents the combined approach for face frontal view estimation from video sequences in a multiview camera setup. This task is important for person identification by face image in video surveillance systems. Face tracking algorithm was developed based on optical flow and cascade face detector. We also found way to estimate quality of face detection. This quality is used as base for best frontal view estimation.

References

H. A. Rowley, S. Baluja, and T. Kanade, Neural network-based face detection, IEEE Trans. Pattern Analogous. Machine Intelligence, (1998). pp. 23-38.

P. Viola and M. J. Jones, Robust real-time face detection, International Journal of Computer Vision, (57) (2004).pp. 137-154.

R. Lienhart and J. Maydt, An extended set of Haar-like features for rapid object detection, IEEE International Conference on Image Processing, (2002). pp. 900-903.

C. Huang, H. Ai, Y. Li, and S. Lao, Vector boosting for rotation invariant multi-view face detection, IEEE International Conference on Computer Vision, (2005). pp. 446-453.

A. Sachenko, I. Paliy, Y. Kurylyak, V. Kapura, R. Sadykhov, and D. Lamovsky, Face detection algorithms for video-surveillance system, IEEE Workshop on Intelligent Data Acquisition and Advance Computing Systems: Technology and Applications Dortmund, Dortmund, Germany, (6-8 September 2007). – pp. 594-598.

C. Cheng and S. Lai, An integrated approach to 3D face model reconstruction from video, IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, Washington, USA, (2001), p. 16.

T. Chen, Audiovisual speech processing, IEEE Signal Processing Magazine, (18) (2001). pp. 9-21.

Y. Zhai and M. Shah, Visual attention detection in video sequences using spatiotemporal cues, 14th annual ACM international conference on Multimedia, Santa Barbara, USA, (2006). pp. 815-824.

F. Ren, N. Nagano, D. B. Bracewell, S. Kuroiwa, T. Tanioka, Z. Zhang, and C. Zong, Facial feature based expression recognition for an effective interface, Artificial Intelligence and Soft Computing Conference, Spain, (2005).

P. Viola and M. Jones, Robust real-time object detection, Second international workshop on statistical and computational theories of vision – modeling, learning, computing, and sampling, Vancouver, Canada, (2001).

A. Bruhn, J. Weickert, and C. Schnorr, Lucas/Kanade Meets Horn/Schunck: Combining local and global optic flow methods, International Journal of Computer Vision, (6) (2005). pp. 211-231.

B. K. P. Horn and B. G. Schunk, Determining optical flow, Artificial Intelligence, (17) (1981). pp. 185-203.

Downloads

Published

2011-12-20

How to Cite

Lamovsky, D. V., Sadykhov, R. K., Kharlanov, V. A., & Kirienko, A. S. (2011). COMBINED APPROACH FOR FACE FRONTAL VIEW ESTIMATION FOR REAL TIME FACES DETECTION IN MULTICAMERA SYSTEM. International Journal of Computing, 10(3), 209-215. https://doi.org/10.47839/ijc.10.3.750

Issue

Section

Articles