Abstract
This paper presents novel features for face detection in the paradigm of AdaBoost algorithm. Features are multi-dimensional histograms computed from a set of rectangles in the filtered images, and they represent marginal distributions of these rectangles. The filter banks consist of intensity, Laplacian of Gaussian (Difference of Gaussians), and Gabor filters, aiming at capturing spatial and frequency properties of human faces at different scales and different orientations. The best features selected by AdaBoost, pairs of filter and rectangle, can thus be interpreted as boosted marginal distributions of human faces. The result of preliminary experiments demonstrate that the selected features are much more powerful to describe the face pattern than the simple features of Viola and Jones and some variants which can only capture several moments of ONE dimensional histogram in intensity images.
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References
Daugman, J.G.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional cortical filters. Journal Opt. Soc. Amer. 2, 1160–1169 (1985)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Machine Learning. Proceedings of the Thirteenth International Conference, pp. 148–156 (1996)
Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From Few To Many: Generative Models For Recognition Under Variable Pose and Illumination. In: IEEE Int. Conf. on Automatic Face and Gesture Recognition, pp. 277–284 (2000)
Li, S.Z., Zhang, Z.: FloatBoost Learning and Statistical Face Detection. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1112–1223 (2004)
Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection. MRL Technical Report. Microprocessor Research Lab, Intel Labs (2002)
Liu, C., Wechsler, H.: Gabor feature based classication using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Processing 11, 467–476 (2002)
Lyons, M.J., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with gabor wavelets. In: Third IEEE Int. Conf. on Automatic Face and Gesture Recognition, Nara Japan (1998)
Martinez, A., Benavente, R.: The AR Face Database. Technical Report. Purdue Univ. (1998)
Murphy, K., Torralba, A., Freeman, W.T.: Using the forest to see the trees: a graphical model relating features, objects, and scenes. Advances in Neural Information Processing Systems (NIPS) 16 (2003)
Rowley, H.A.: Neural Network-Based Face Detection. PhD thesis, Carnegie Mellon Univ. (1999)
Samaria, F., Harter, A.: Parameterisation of a stochastic model for human face identification. In: Proceedings of the 2nd IEEE Workshop on Applications of Computer Vision (1994)
Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression (PIE) database. In: Proc. of the IEEE International Conference on Automatic Face and Gesture Recognition (2002)
Viola, P., Jones, M.J.: Robust Real-Time Face Detection. International Journal of Computer Vision 57, 137–154 (2004)
Wu, J., Rehg, J.M., Mullin, M.D.: Learning a Rare Event Detection Cascade by Direct Feature Selection. Advances in Neural Information Processing Systems (NIPS) 16 (2004)
Yang, M.H., Kriegman, D.J., Ahuja, N.: Detecting Faces in Images: A Survey. IEEE Trans. Pattern Analysis and Machine Intelligence 24, 34–58 (2002)
Zhu, S.C., Wu, Y.N., Mumford, D.B.: Filters, Random Field and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling. International Journal of Computer Vision 27, 107–126 (1998)
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Haijing, Li, P., Zhang, T. (2005). Proposal of Novel Histogram Features for Face Detection. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_38
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DOI: https://doi.org/10.1007/11552499_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
Online ISBN: 978-3-540-31999-3
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