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Deep, Landmark-Free FAME: Face Alignment, Modeling, and Expression Estimation

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Abstract

We present a novel method for modeling 3D face shape, viewpoint, and expression from a single, unconstrained photo. Our method uses three deep convolutional neural networks to estimate each of these components separately. Importantly, unlike others, our method does not use facial landmark detection at test time; instead, it estimates these properties directly from image intensities. In fact, rather than using detectors, we show how accurate landmarks can be obtained as a by-product of our modeling process. We rigorously test our proposed method. To this end, we raise a number of concerns with existing practices used in evaluating face landmark detection methods. In response to these concerns, we propose novel paradigms for testing the effectiveness of rigid and non-rigid face alignment methods without relying on landmark detection benchmarks. We evaluate rigid face alignment by measuring its effects on face recognition accuracy on the challenging IJB-A and IJB-B benchmarks. Non-rigid, expression estimation is tested on the CK+ and EmotiW’17 benchmarks for emotion classification. We do, however, report the accuracy of our approach as a landmark detector for 3D landmarks on AFLW2000-3D and 2D landmarks on 300W and AFLW-PIFA. A surprising conclusion of these results is that better landmark detection accuracy does not necessarily translate to better face processing. Parts of this paper were previously published by Tran et al. (2017) and Chang et al. (2017, 2018).

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Notes

  1. FPN, bundled with rendering and alignment code, publicly available from: https://github.com/fengju514/Face-Pose-Net.

  2. The train/test partitions of PIFA are available at http://cvlab.cse.msu.edu/project-pifa.html.

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Acknowledgements

This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA 2014-14071600011. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purpose notwithstanding any copyright annotation thereon.

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Authors and Affiliations

  1. Information Sciences Institute (ISI), USC, Marina Del Rey, CA, USA

    Iacopo Masi

  2. Institute for Robotics and Intelligent Systems, USC, Los Angeles, CA, USA

    Feng-Ju Chang, Anh Tuan Tran, Ram Nevatia & Gérard Medioni

  3. Open University of Israel, Ra’anana, Israel

    Tal Hassner

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  1. Feng-Ju Chang
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Chang, FJ., Tran, A.T., Hassner, T. et al. Deep, Landmark-Free FAME: Face Alignment, Modeling, and Expression Estimation. Int J Comput Vis 127, 930–956 (2019). https://doi.org/10.1007/s11263-019-01151-x

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