CONFIDENCE MEASURE FOR AUTOMATIC FACE RECOGNITION

Ladislav Lenc, Pavel Král

Abstract

This paper deals with the use of confidence measure for Automatic Face Recognition (AFR). AFR is realized by the adapted Kepenecki face recognition approach based on the Gabor wavelet transform. This work is motivated by the fact that obtained recognition rate on the real-world corpus is only about 50% which is not sufficient for our application, a system for automatic labelling of the photographs in a large database. The main goal of this work is thus the proposition of the post-processing of the classification result in order to remove the “incorrectly” classified face images. We show that the use of confidence measure to filter out incorrectly recognized faces is beneficial. Two confidence measures are proposed and evaluated on the Czech News Agency (¡CTK) corpus. Experimental results confirm the benefit of the use of confidence measure for the automatic face recognition task.

References

  1. Bledsoe, W. W. (1966). Man-machine facial recognition. Technical report, Panoramic Research Inc., Palo Alto, CA.
  2. Bolme, D. S. (2003). Elastic Bunch Graph Matching. PhD thesis, Colorado State University.
  3. Jiang, H. (2005). Confidence measures for speech recognition: a survey. Speech Communication, 45(4):455- 470.
  4. Kanade, T. (1977). Computer recognition of human faces. Birkhauser Verlag.
  5. Kepenekci, B. (2001). Face Recognition Using Gabor Wavelet Transform. PhD thesis, The Middle East Technical University.
  6. Lades, M., Vorbrüggen, J. C., Buhmann, J., Lange, J., and von der Malsburg, C. (1993). Distortion invariant object recognition in the dynamic link architecture. IEEE Transactions On Computers.
  7. Lawrence, S., Giles, S., Tsoi, A., and Back, A. (1997). Face recognition: A convolutional neural network approach. IEEE Trans. on Neural Networks.
  8. Lleida, E. and Rose, R. C. (1996). Likelihood Ratio Decoding and Confidence Measures for Continuous Speech Recognition. In ICSLP'96, volume 1, pages 478-481, Philadelphia, USA.
  9. Nefian, A. V. and Hayes, M. H. (1998). Hidden markov models for face recognition. In IEEE International Conference on Acoustics, Speech, and Signal Processing.
  10. Shen, L. (2005). Recognizing Faces - An Approach Based on Gabor Wavelets. PhD thesis, University of Nottingham.
  11. Shen, L. and Bai, L. (2006). A review on gabor wavelets for face recognition. Pattern Analysis & Applications.
  12. Sirovich, L. and Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America, 4.
  13. Turk, M. A. and Pentland, A. P. (1991). Face recognition using eigenfaces. In IEEE Computer Society Conference on In Computer Vision and Pattern Recognition. Computer Vision and Pattern Recognition.
  14. Wiskott, L., Fellous, J.-M., Krüger, N., and von der Malsburg, C. (1999). Face recognition by elastic bunch graph matching. Intelligent Biometric Techniques in Fingerprint and Face Recognition.
Download


Paper Citation


in Harvard Style

Lenc L. and Král P. (2011). CONFIDENCE MEASURE FOR AUTOMATIC FACE RECOGNITION . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011) ISBN 978-989-8425-79-9, pages 357-360. DOI: 10.5220/0003690403650368


in Bibtex Style

@conference{kdir11,
author={Ladislav Lenc and Pavel Král},
title={CONFIDENCE MEASURE FOR AUTOMATIC FACE RECOGNITION},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)},
year={2011},
pages={357-360},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003690403650368},
isbn={978-989-8425-79-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)
TI - CONFIDENCE MEASURE FOR AUTOMATIC FACE RECOGNITION
SN - 978-989-8425-79-9
AU - Lenc L.
AU - Král P.
PY - 2011
SP - 357
EP - 360
DO - 10.5220/0003690403650368