Improving face detection

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche


Referenz

Machado, P., Correia, J., Romero, J.: Improving face detection. In: Moraglio, A., Silva, S., Krawiec, K., Machado, P., Cotta, C. (eds.) EuroGP 2012. LNCS, vol. 7244, pp. 73–84. Springer, Heidelberg (2012)

DOI

http://dx.doi.org/10.1007/978-3-642-29139-5_7

Abstract

A novel Genetic Programming approach for the improvement of the performance of classifier systems through the synthesis of new training instances is presented. The approach relies on the ability of the Genetic Programming engine to identify and exploit shortcomings of classifier systems, and generate instances that are misclassified by them. The addition of these instances to the training set has the potential to improve classifier’s performance. The experimental results attained with face detection classifiers are presented and discussed. Overall they indicate the success of the approach.

Extended Abstract

Bibtex

@Inbook{Machado2012,
author="Machado, Penousal and Correia, Jo{\~a}o and Romero, Juan",
editor="Moraglio, Alberto and Silva, Sara and Krawiec, Krzysztof and Machado, Penousal and Cotta, Carlos",
title="Improving Face Detection",
bookTitle="Genetic Programming: 15th European Conference, EuroGP 2012, M{\'a}laga, Spain, April 11-13, 2012. Proceedings",
year="2012",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="73--84",
isbn="978-3-642-29139-5",
doi="10.1007/978-3-642-29139-5_7",
url="http://dx.doi.org/10.1007/978-3-642-29139-5_7 http://de.evo-art.org/index.php?title=Improving_face_detection"
}

Used References

1. Baro, X., Escalera, S., Vitria, J., Pujol, O., Radeva, P.: Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification. IEEE Transactions on Intelligent Transportation Systems 10(1), 113–126 (2009) http://dx.doi.org/10.1109/TITS.2008.2011702

2. Chen, J., Chen, X., Gao, W.: Resampling for face detection by self-adaptive genetic algorithm. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, pp. 822–825 (August 2004)

3. Dubey, D.: Face detection using genetic algorithm and neural network. International Journal of Science and Advanced Technology 1(6), 104–109 (2011) ISSN 2221-8386

4. Freund, Y., Schapire, R.E.: A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting (1995)

5. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 643–660 (2001) http://dx.doi.org/10.1109/34.927464

6. Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust Face Detection Using the Hausdorff Distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001) http://dx.doi.org/10.1007/3-540-45344-X_14

7. Krawiec, K., Howard, D., Zhang, M.: Overview of Object Detection and Image Analysis by Means of Genetic Programming Techniques. In: Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007, pp. 779–784 (2007)

8. Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 297–304. Springer, Heidelberg (2003) http://dx.doi.org/10.1007/978-3-540-45243-0_39

9. Lienhart, R., Maydt, J.: An Extended Set of Haar-Like Features for Rapid Object Detection. In: Proceedings of the 2002 International Conference on Image Processing, vol. 1, pp. 900–903 (2002)

10. Machado, P., Cardoso, A.: All the truth about NEvAr. Applied Intelligence, Special Issue on Creative Systems 16(2), 101–119 (2002)MATH

11. Machado, P., Romero, J., Manaris, B.: Experiments in Computational Aesthetics. In: The Art of Artificial Evolution, Springer, Heidelberg (2007)

12. Mayer, H.A., Schwaiger, R.: Towards the evolution of training data sets for artificial neural networks. In: IEEE International Conference on Evolutionary Computation, pp. 663–666 (April 1997)

13. Papageorgiou, C.P., Oren, M., Poggio, T.: A general framework for object detection. In: Sixth International Conference on Computer Vision, pp. 555–562 (January 1998)

14. Sha, S., Jianer, C., Ling, Q., Sanding, L.: Evolutionary mechanism and implemention for recognition of objects in dynamic vision. In: 4th International Conference on Computer Science Education, ICCSE 2009, pp. 178–182 (2009)

15. Sims, K.: Artificial Evolution for Computer Graphics. ACM Computer Graphics 25, 319–328 (1991) http://dx.doi.org/10.1145/127719.122752

16. Spector, L., Alpern, A.: Criticism, culture, and the automatic generation of artworks. In: Proceedings of Twelfth National Conference on Artificial Intelligence, pp. 3–8. AAAI Press/MIT Press, Seattle, Washington (1994)

17. Teller, A., Veloso, M.: Algorithm evolution for face recognition: what makes a picture difficult. In: IEEE International Conference on Evolutionary Computation 1995 (1995)

18. Ventura, D., Andersen, T., Martinez, T.R.: Using evolutionary computation to generate training set data for neural networks. In: Proceedings of the International Conference on Neural Networks and Genetic Algorithms, pp. 468–471 (1995)

19. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, vol. 1, pp. I–511–I–518 (2001)

20. Yang, M.-H., Roth, D., Ahuja, N.: A snow-based face detector. In: Advances in Neural Information Processing Systems 12, pp. 855–861. MIT Press (2000)

Links

Full Text

internal file


Sonstige Links