Abstract
In this paper we propose an integrated system for face detection and face recognition based on improved versions of state-of-the-art statistical learning techniques such as Boosting and LDA. Both the detection and the recognition processes are performed on facial features (e.g., the eyes, the nose, the mouth, etc) in order to improve the recognition accuracy and to exploit their statistical independence in the training phase. Experimental results on real images show the superiority of our proposed techniques with respect to the existing ones in both the detection and the recognition phase.
Chapter PDF
References
Bassiou, N., Kotropoulos, C., Kosmidis, T., Pitas, I.: Frontal face detection using support vector machines and back-propagation neural networks. In: ICIP (1), Thessaloniki, Greece, October 7–10, 2001, pp. 1026–1029 (2001)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Brunelli, R., Poggio, T.: Face recognition: Features versus templates. IEEE Transaction on Pattern Analysis and Machine Intelligence 15(10), 1042–1052 (1993)
Cristinacce, D., Cootes, T., Scott, I.: A multi-stage approach to facial feature detection. In: British Machine Vision Conference (BMVC 2004), pp. 277–286 (2004)
Duda, R.O., Hart, P.E., Strorck, D.G.: Pattern classification, 2nd edn. Wiley Interscience, Hoboken (2000)
University of Essex. The Essex Database (1994), http://cswww.essex.ac.uk/mv/allfaces/faces94.html
Phillips, P., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing 16(5), 295–306 (1998)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: ICML, Bari, Italy, July 3–6, 1996, pp. 148–156 (1996)
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: A statistical view of boosting. The Annals of Statistics 28, 337–374 (2000)
Li, S.Z., Zhang, Z.: Floatboost learning and statistical face detection. IEEE Trans. Pattern Anal. Machine Intell. 26(9), 1112–1123 (2004)
Nefian, A., Hayes, M.: Face detection and recognition using hidden markov models. In: ICIP, Chicago, IL, USA, October 4–7, 1998, vol. 1, pp. 141–145 (1998)
ATeT Laboratories Cambridge. The ORL Face Database (2004), http://www.camorl.co.uk/facedatabase.html
Schapire, R.E.: Theoretical views of boosting and applications. In: Watanabe, O., Yokomori, T. (eds.) ALT 1999. LNCS, vol. 1720, pp. 13–25. Springer, Heidelberg (1999)
Smach, F., Abid, M., Atri, M., Mitéran, J.: Design of a neural networks classifier for face detection. Journal of Computer Science 2(3), 257–260 (2006)
Viola, P.A., Jones, M.J.: Fast and robust classification using asymmetric adaboost and a detector cascade. In: NIPS, Vancouver, British Columbia, Canada, December 3–8, 2001, pp. 1311–1318 (2001)
Viola, P.A., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: CVPR (1), Kauai, HI, USA, December 8–14, 2001, pp. 511–518 (2001)
Wiskott, L., Fellous, J.M., Malsburg, C.V.D.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Machine Intell. 19, 775–779 (1997)
Xiang, C., Fan, X.A., Lee, T.H.: Face recognition using recursive fisher linear discriminant. IEEE Transactions on Image Processing 15(8), 2097–2105 (2006)
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. CM Computing Surveys 35(4), 399–458 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Micheloni, C., Sangineto, E., Cinque, L., Foresti, G.L. (2009). Improved Statistical Techniques for Multi-part Face Detection and Recognition. In: Salberg, AB., Hardeberg, J.Y., Jenssen, R. (eds) Image Analysis. SCIA 2009. Lecture Notes in Computer Science, vol 5575. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02230-2_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-02230-2_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02229-6
Online ISBN: 978-3-642-02230-2
eBook Packages: Computer ScienceComputer Science (R0)