Abstract
In comparison with 2D face images, 3D face models have the advantage of being illumination and pose invariant, which provides improved capability of handling changing environments in practical surveillance. Feature detection, as the initial process of reconstructing 3D face models from 2D uncalibrated image sequences, plays an important role and directly affects the accuracy and robustness of the resulting reconstruction. In this paper, we propose an automated scene-specific selection algorithm that adaptively chooses an optimal feature detector according to the input image sequence for the purpose of 3D face reconstruction. We compare the performance of various feature detectors in terms of accuracy and robustness of the sparse and dense reconstructions. Our experimental results demonstrate the effectiveness of the proposed selection method from the observation that the chosen feature detector produces 3D reconstructed face models with superior accuracy and robustness to image noise.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Pollefeys, M., Gool, L., van Vergauwen, M., Verbiest, F., Cornelis, K., Tops, J.: Visual modeling with a hand-held camera. Int’l J. of Computer Vision 59, 207–232 (2004)
Kien, D.T.: A review of 3D reconstruction from video sequences. MediaMill3D Technical Report, University of Amsterdam (2005)
Lu, X., Jain, A.K., Colbry, D.: Matching 2.5D face scans to 3D models. IEEE Trans. on Pattern Analysis and Machine Intelligence 28, 31–43 (2006)
Hu, Y., Jiang, D., Yan, S., Zhang, L., Zhang, H.: Automatic 3D reconstruction for face recognition. In: IEEE Int’l Conf. on Automatic Face and Gesture Recognition, Washington D.C, pp. 843–848 (2004)
Chowdhury, A.R., Chellappa, R., Krishnamurthy, S., Vo, T.: 3D face reconstruction from video using a generic model. In: IEEE Int’l Conf. on Multimedia and Expo, Lausanne, Switzerland, pp. 449–452 (2002)
Bozdogan, H.: Akaike’s information criterion and recent developments in information complexity. J. of Mathematical Psychology 44, 62–69 (2000)
Sukumar, S.R., Bozdogan, H., Page, D., Koschan, A., Abidi, A.: MuFeSaC: Learning when to use which feature detector. In: Accepted to the IEEE International Conference on Image Processing, San Antonio, TX (2007)
Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. Int’l J. of Computer Vision 37, 151–172 (2000)
Mahamud, S., Hebert, M., Omori, Y., Ponce, J.: Provably-convergent iterative methods for projective structure from motion. In: IEEE Conf. on Computer Vision and Pattern Recognition, Kauai, Hawaii, pp. 1018–1025 (2001)
Sturm, P., Triggs, B.: A factorization based algorithm for multi-image projective structure and motion. In: European Conf. on Computer Vision, Cambridge, England, pp. 709–720 (1996)
Pollefeys, M., Koch, R., van Gool, L.: Self-calibration and metric reconstruction in spite of varying and unknown intrinsic camera parameters. In: IEEE Int’l Conf. on Computer Vision, Bombay, India, pp. 90–95 (1998)
He, X.C., Yung, N.H.C.: Curvature scale space corner detector with adaptive threshold and dynamic region of support. In: Int’l Conf. on Pattern Recognition, Cambridge, England, pp. 791–794 (2004)
Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int’l J. of Computer Vision 1, 63–68 (2004)
Kovesi, P.: Phase congruency detects corners and edges. In: Australian Pattern Recognition Society Conf., Sydney, Australian, pp. 309–318 (2003)
Rosten, E., Drummond, T.: Machine learning for high speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yao, Y., Sukumar, S., Abidi, B., Page, D., Koschan, A., Abidi, M. (2007). Automated Scene-Specific Selection of Feature Detectors for 3D Face Reconstruction. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4841. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76858-6_47
Download citation
DOI: https://doi.org/10.1007/978-3-540-76858-6_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76857-9
Online ISBN: 978-3-540-76858-6
eBook Packages: Computer ScienceComputer Science (R0)