Robust Registration of Dynamic Facial Sequences.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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Sariyanidi, E., Gunes, H., & Cavallaro, A. (2017). Robust Registration of Dynamic Facial Sequences.. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 26 1708-1722. https://doi.org/10.1109/tip.2016.2639448
Accurate face registration is a key step for several image analysis applications. However, existing registration methods are prone to temporal drift errors or jitter among consecutive frames. In this paper we propose an iterative rigid registration framework that estimates the misalignment with trained regressors. The input of the regressors is a robust motion representation that encodes the motion between a misaligned frame and the reference frame(s), and enables reliable performance under non-uniform illumination variations. Drift errors are reduced when the motion representation is computed from multiple reference frames. Furthermore, we use the $L_2$ norm of the representation as a cue for performing coarse-to-fine registration efficiently. Importantly, the framework can identify registration failures and correct them. Experiments show that the proposed approach achieves significantly higher registration accuracy than state-of-the-art techniques in challenging sequences.
The research work of Evangelos Sariyanidi and Hatice Gunes has been partially supported by the EPSRC under its IDEAS Factory Sandpits call on Digital Personhood (Grant Ref.: EP/L00416X/1).
EPSRC (via University of Exeter) (EP/L00416X/1)
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External DOI: https://doi.org/10.1109/tip.2016.2639448
This record's URL: https://www.repository.cam.ac.uk/handle/1810/262669