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The Impact of Removing Head Movements on Audio-visual Speech Enhancement
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In: ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.inria.fr/hal-03551610 ; ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE Signal Processing Society, May 2022, Singapore, Singapore. pp.1-5 (2022)
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Expression-preserving face frontalization improves visually assisted speech processing ...
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Robust Face Frontalization For Visual Speech Recognition
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In: International Conference on Computer Vision Workshops ; https://hal.inria.fr/hal-03326002 ; International Conference on Computer Vision Workshops, IEEE, Oct 2021, Montreal - Virtual, Canada ; http://iccv2021.thecvf.com/home (2021)
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Robust Face Frontalization For Visual Speech Recognition
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In: ICCV 2021 - International Conference on Computer Vision Workshops ; https://hal.inria.fr/hal-03326002 ; ICCV 2021 - International Conference on Computer Vision Workshops, IEEE, Oct 2021, Montreal - Virtual, Canada. pp.1-16 ; http://iccv2021.thecvf.com/home (2021)
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| 5 |
Robust Face Frontalization For Visual Speech Recognition
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In: ICCV 2021 - International Conference on Computer Vision Workshops ; https://hal.inria.fr/hal-03326002 ; ICCV 2021 - International Conference on Computer Vision Workshops, IEEE, Oct 2021, Montreal - Virtual, Canada. pp.1-16 ; http://iccv2021.thecvf.com/home (2021)
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Face Frontalization Based on Robustly Fitting a Deformable Shape Model to 3D Landmarks
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In: https://hal.archives-ouvertes.fr/hal-02980346 ; 2020 (2020)
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Abstract:
Submitted to IEEE Transactions on Multimedia ; Face frontalization consists of synthesizing a frontally-viewed face from an arbitrarily-viewed one. The main contribution of this paper is a robust face alignment method that enables pixel-to-pixel warping. The method simultaneously estimates the rigid transformation (scale, rotation, and translation) and the non-rigid deformation between two 3D point sets: a set of 3D landmarks extracted from an arbitrary-viewed face, and a set of 3D landmarks parameterized by a frontally-viewed deformable face model. An important merit of the proposed method is its ability to deal both with noise (small perturbations) and with outliers (large errors). We propose to model inliers and outliers with the generalized Student's t-probability distribution function-a heavy-tailed distribution that is immune to non-Gaussian errors in the data. We describe in detail the associated expectation-maximization (EM) algorithm that alternates between the estimation of (i) the rigid parameters, (ii) the deformation parameters, and (iii) the t-distribution parameters. We also propose to use the zero-mean normalized cross-correlation, between a frontalized face and the corresponding ground-truth frontally-viewed face, to evaluate the performance of frontalization. To this end, we use a dataset that contains pairs of profile-viewed and frontally-viewed faces. This evaluation, based on direct image-to-image comparison, stands in contrast with indirect evaluation, based on analyzing the effect of frontalization on face recognition. 1
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Keyword:
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD]; [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
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URL: https://hal.archives-ouvertes.fr/hal-02980346/file/Kang-arxiv2020-V1.pdf
https://hal.archives-ouvertes.fr/hal-02980346/document
https://hal.archives-ouvertes.fr/hal-02980346
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