Abstract
In this paper we address the challenge of performing face recognition of a probe set of non-frontal images by performing automatic pose correction using Active Appearance Models (AAMs) and matching against a set of enrollment gallery of frontal images. Active Appearance Models are used as a way to register and fitting the model to extract 79 facial fiducial points which are then used to partition the face into a wire-mesh of triangular polygons which are used to warp the facial image to a frontal facial mesh pose. We extend to use View-Based Active Appearance Models (VBAAMs) which are able to represent a preset number of poses better than synthesizing a single AAM to handle all possible pose variations. We demonstrate our approach is able to achieve high performance results on the new larger CMU Multi-PIE dataset using 249 different people with 15 different pose angles and 20 different illumination variations under 2 different expressions (total of 149400 images). We show that our proposed pose correction approach can improve the recognition performance of many baseline algorithms such PCA, LDA, Kernel Discriminant Analysis (KDA) on the CMU Multi-PIE dataset.
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References
Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face Recognition: A Literature Survey. In: ACM Computing Surveys, pp. 399–458 (2003)
Moghaddam, B., Pentland, A.: Probabilistic visual learning for object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 696–710 (1997)
Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3, 72–86 (1991)
Cootes, T.F., Walker, K., Taylor, C.J.: View-Based Active Appearance Models. In: Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition (2000)
Bowyer, K.W., Chang, K., Flynn, P.: Evaluation of Multimodal 2D+3D Face Biometrics. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(4), 619–624 (2005)
Blanz, V., Vetter, T.: Face Recognition Based on Fitting a 3D Morphable Model. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(9), 1063–1074 (2003)
Cootes, T., Edwards, G., Taylor, C.: Active appearance models. Proceedings of the European Conference on Computer Vision 2, 484–498 (1998)
Matthews, I., Baker, S.: Active Appearance Models Revisited. International Journal of Computer Vision 60(2), 135–164 (2004)
Hu, C., Xiao, J., Matthews, I., Baker, S., Cohn, J., Kanade, T.: Fitting a Single Active Appearance Model Simultaneously to Multiple Images. In: Proceedings of the British Machine Vision Conference (September 2004)
Gross, R., Brajovic, V.: An Image Preprocessing Algorithm for Illumination Invariant Face Recognition. In: 4th International Conference on Audio- and Video-Based Biometric Person Authentication (AVBPA), June 2003, Springer, Heidelberg (2003)
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Heo, J., Savvides, M. (2008). Face Recognition Across Pose Using View Based Active Appearance Models (VBAAMs) on CMU Multi-PIE Dataset. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds) Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science, vol 5008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79547-6_51
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DOI: https://doi.org/10.1007/978-3-540-79547-6_51
Publisher Name: Springer, Berlin, Heidelberg
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