A NEURAL NETWORK-BASED SYSTEM FOR FACE DETECTION IN LOW QUALITY WEB CAMERA IMAGES

Ioanna-Ourania Stathopoulou, George A. Tsihrintzis

Abstract

The rapid and successful detection and localization of human faces in images is a prerequisite to a fully automated face image analysis system. In this paper, we present a neural network–based face detection system which arises from the outcome of a comparative study of two neural network models of different architecture and complexity. The fundamental difference in the construction of the two models lies in approaching the face detection problem either by seeking a general solution based on the full-face image or by composing the solution through the resolution of specific portions/characteristics of the face. The proposed system is based on the brightness contrasts between specific regions of the human face. We show that the second approach, even though more complicated, exhibits better performance in terms of detection and false-positive rates. We tested our system with low quality face images acquired with web cameras. The image test set includes both front and side view images of faces forming either a neutral or one of the “smile”, “surprise”, “disgust”, “scream”, “bored-sleepy”, “angry”, and “sad” expressions. The system achieved high face detection rates, regardless of facial expression or face view.

References

  1. Belongie S. (2000). “Notes on Clustering Point-sets with Normalized Cuts”.
  2. Castrillon, M. et al. (2007). ENCARA2: Real-time detection of multiple faces at different resolutions in video streams, Journal of Visual Communication and Image Representation -In Press.
  3. Colmenarez, A. J. and Huang, T.S. (1997). “Face detection with information-based maximum discrimination”, Computer Vision and Pattern Recognition, 782-787.
  4. Gender Classification (Databases): http://ise0.stanford.edu/class/ee368a_proj00/project1 5/intro.html http://ise0.stanford.edu/class/ee368a_proj00/project1 5/append_a.html
  5. Huang, Lin-Lin and Shimizu, Akinobu (2006). A multiexpert approach for robust face detection. Pattern Recognition 39(9): 1695-1703.
  6. Juell, P. and Marsh, R. (1996). “A hierarchical neural network for human face detection”, Pattern Recognition 29 (5), 781-787.
  7. Kadoury, Samuel and Levine, Martin D. (2007). “Face detection in gray scale images using locally linear embeddings”, Computer Vision and Image Understanding, 105: 1-20.
  8. Lee, S.Y., Ham, Y.K., Park, R.H. (1996). “Recognition of human front faces using knowledge-based feature extraction and neuro-fuzzy algorithm”, Pattern Recognition 29,1863-1876 (11) .
  9. Leung, T.K., Burl, M.C. and Perona, P. (1995). “Finding faces in cluttered scenes using random labeled graph matching”, Fifth International Conference on Computer Vision, pages 637-644, Cambridge, Massachusetts, IEEE Computer Society Press.
  10. Lin, C. and Fan, K. (2001). “Triangle-based approach to the detection of human face”, Pattern Recognition 34, 1271-1284.
  11. Phimoltares, S., Lursinsap, C., Chamnongthai, K. (2007, May). “Face detection and facial feature localization without considering he appearance of image context”, Image and Vision Computing Volume 25 , Issue 5: 741-753.
  12. Rowley, H.A., Baluja, S., and Kanade T. (1997). “Rotation Invariant Neural Network-Based Face Detection” CMU-CS-97-201.
  13. Rowley, H.A., Baluja, S., and Kanade T. (1998). “Neural Network-based face detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1).
  14. Shi, J. and Malik, J. (2000). “Normalized Cuts and Image Segmentation”, IEEE Transactions On Pattern Analysis and Machine Intelligence, Vol. 22(8).
  15. Sung, K.K., Poggio, T. (1994). “Example-based learning for view-based human face detection”, Proceedings on Image Understanding Workshop, Monterey, CA, 843- 850.
  16. Yang, G. and Huang, T.S. (1994). “Human face detection in a complex background”, Pattern Recognition, 27(1):53-63 .
  17. Yang, M.-H., Ahuja, N. (2003). “Face Detection and Gesture Recognition for Human- computer Interaction”, Kluver Academic Publishers.
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Paper Citation


in Harvard Style

Stathopoulou I. and A. Tsihrintzis G. (2007). A NEURAL NETWORK-BASED SYSTEM FOR FACE DETECTION IN LOW QUALITY WEB CAMERA IMAGES . In Proceedings of the Second International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2007) ISBN 978-989-8111-13-5, pages 53-58. DOI: 10.5220/0002134100530058


in Bibtex Style

@conference{sigmap07,
author={Ioanna-Ourania Stathopoulou and George A. Tsihrintzis},
title={A NEURAL NETWORK-BASED SYSTEM FOR FACE DETECTION IN LOW QUALITY WEB CAMERA IMAGES},
booktitle={Proceedings of the Second International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2007)},
year={2007},
pages={53-58},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002134100530058},
isbn={978-989-8111-13-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2007)
TI - A NEURAL NETWORK-BASED SYSTEM FOR FACE DETECTION IN LOW QUALITY WEB CAMERA IMAGES
SN - 978-989-8111-13-5
AU - Stathopoulou I.
AU - A. Tsihrintzis G.
PY - 2007
SP - 53
EP - 58
DO - 10.5220/0002134100530058