Face Recognition under Real-world Conditions

Ladislav Lenc, Pavel Král

Abstract

This paper deals with Automatic Face Recognition (AFR). The main contribution of this work consists in the evaluation of our two previously proposed AFR methods in real conditions. At first, we compare and evaluate the recognition accuracy of two AFR methods on well-controlled face database. Then we compare these results with the recognition accuracy on a real-world database of comparable size. For such comparison, we use a sub-set of the newly created Czech News Agency (ˇCTK) database. This database is created from the real photos acquired by the ˇCTK and the creation of this corpus represents the second contribution of this work. The experiments show the significant differences in the results on the controlled and real-world data. 100% accuracy is achieved on the ORL database while only 72.7% is the best score for the ˇCTK database. Further experiments show, how the recognition rate is influenced by the number of training images for each person and by the size of the database. We also demonstrate, that the recognition rate decreases significantly with larger database. We propose a confidence measure technique as a solution to identify and to filter-out the incorrectly recognized faces. We further show that confidence measure is very beneficial for AFR under real conditions.

References

  1. Aly, M. (2006). Face recognition using sift features. CNS/Bi/EE report 186.
  2. Belhumeur, P. N., Hespanha, J. a. P., and Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence.
  3. Bolme, D. S. (2003). Elastic Bunch Graph Matching. PhD thesis, Colorado State University.
  4. Jiang, H. (2005). Confidence measures for speech recognition: a survey. Speech Communication, 45(4):455- 470.
  5. Kepenekci, B. (2001). Wavelet Transform. Technical University.
  6. Král, P., Cerisara, C., and Klec?ková, J. (2006). Automatic Dialog Acts Recognition based on Sent ence Structure. In ICASSP'06, pages 61-64, Toulouse, France.
  7. Krizaj, J., Struc, V., and Pavesic, N. (2010). Adaptation of sift features for robust face recognition.
  8. 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.
  9. Lawrence, S., Giles, S., Tsoi, A., and Back, A. (1997). Face recognition: A convolutional neural network approach. IEEE Trans. on Neural Networks.
  10. Lenc, L. and Král, P. (2011). Confidence measure for automatic face recognition. In International Conference on Knowledge Discovery and Information Retrieval, Paris, France.
  11. Lenc, L. and Král, P. (2012a). Gabor wavelets for automatic face recognition. In 38th International Conference on Current Trends in Theory and Practice of Computer Science, S?pindleru°v MlÉn, Czech Republic.
  12. Lenc, L. and Král, P. (2012b). Novel matching methods for automatic face recognition using sift. In 8th AIAI (Artificial Intelligence Applications and Innovations) Confence, Halkidiki, Greece.
  13. Li, S. and Jain, A. (2005). Handbook of face recognition. Springer-Verlag.
  14. 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.
  15. Lowe, D. (2004a). Software for sift.
  16. Lowe, D. G. (1999). Object recognition from local scaleinvariant features. In International Conference on Computer Vision.
  17. Lowe, D. G. (2004b). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 2.
  18. Nefian, A. V. and Hayes, M. H. (1998). Hidden markov models for face recognition. In IEEE International Conference on Acoustics, Speech, and Signal Processing.
  19. Samaria, F. and Young, S. (1994). Hmm-based architecture for face identification. Image and Vision Computing.
  20. Shen, L. (2005). Recognizing Faces - An Approach Based on Gabor Wavelets. PhD thesis, University of Nottingham.
  21. Shen, L. and Bai, L. (2006). A review on gabor wavelets for face recognition. Pattern Analysis & Applications.
  22. Tan, P.-N., Steinbach, M., and Kumar, V. (2005). Introduction to Data Mining. Addison-Wesley.
  23. 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.
  24. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Conference on Computer Vision and Pattern Recognition.
  25. 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. (2013). Face Recognition under Real-world Conditions . In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-8565-39-6, pages 250-256. DOI: 10.5220/0004237402500256


in Bibtex Style

@conference{icaart13,
author={Ladislav Lenc and Pavel Král},
title={Face Recognition under Real-world Conditions},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2013},
pages={250-256},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004237402500256},
isbn={978-989-8565-39-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Face Recognition under Real-world Conditions
SN - 978-989-8565-39-6
AU - Lenc L.
AU - Král P.
PY - 2013
SP - 250
EP - 256
DO - 10.5220/0004237402500256