Abstract
We propose a low-complexity face synthesis technique which transforms a 2D frontal view image into views at specific poses, without recourse to computationally expensive 3D analysis or iterative fitting techniques that may fail to converge. The method first divides a given image into multiple overlapping blocks, followed by synthesising a non-frontal representation through applying a multivariate linear regression model on a low-dimensional representation of each block. To demonstrate one application of the proposed technique, we augment a frontal face verification system by incorporating multi-view reference (gallery) images synthesised from the frontal view. Experiments on the pose subset of the FERET database show considerable reductions in error rates, especially for large deviations from the frontal view.
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References
Phillips, P., Grother, P., Micheals, R., Blackburn, D., Tabassi, E., Bone, M.: Face recognition vendor test 2002. In: Proc. Int. Workshop on Analysis and Modeling of Faces and Gestures (AMFG), p. 44 (2003)
Cootes, T., Walker, K., Taylor, C.: View-based active appearance models. In: Proc. 4th IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 227–232 (2000)
Shan, T., Lovell, B., Chen, S.: Face recognition robust to head pose from one sample image. In: Proc. ICPR, vol. 1, pp. 515–518 (2006)
Blanz, V., Vetter, T.: Face recognition based on fitting a 3D morphable model. IEEE Trans. Pattern Analysis and Machine Intelligence 25(9), 1063–1074 (2003)
Blanz, V., Grother, P., Phillips, P., Vetter, T.: Face recognition based on frontal views generated from non-frontal images. In: Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 454–461 (2005)
Gonzales, R., Woods, R.: Digital Image Processing, 3rd edn. Prentice-Hall, Englewood Cliffs (2007)
Rice, J.: Mathematical Statistics and Data Analysis, 2nd edn. Duxbury Press (1995)
Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)
Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)
Rodriguez, Y., Cardinaux, F., Bengio, S., Mariethoz, J.: Measuring the performance of face localization systems. Image and Vision Comput. 24, 882–893 (2006)
Bishop, C.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
Sanderson, C.: Biometric Person Recognition: Face, Speech and Fusion. VDM-Verlag (2008)
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Wong, Y., Sanderson, C., Lovell, B.C. (2009). Regression Based Non-frontal Face Synthesis for Improved Identity Verification. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_14
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DOI: https://doi.org/10.1007/978-3-642-03767-2_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03766-5
Online ISBN: 978-3-642-03767-2
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