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

Authors
  • Chang, Feng-Ju1
  • Tran, Anh Tuan1
  • Hassner, Tal2
  • Masi, Iacopo3
  • Nevatia, Ram1
  • Medioni, Gérard1
  • 1 USC, Institute for Robotics and Intelligent Systems, Los Angeles, CA, USA , Los Angeles (United States)
  • 2 Open University of Israel, Ra’anana, Israel , Ra’anana (Israel)
  • 3 USC, Information Sciences Institute (ISI), Marina Del Rey, CA, USA , Marina Del Rey (United States)
Type
Published Article
Journal
International Journal of Computer Vision
Publisher
Springer-Verlag
Publication Date
Feb 13, 2019
Volume
127
Issue
6-7
Pages
930–956
Identifiers
DOI: 10.1007/s11263-019-01151-x
Source
Springer Nature
Keywords
License
Yellow

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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