Solve it or Ignore it? The Challenge of Alignment Distortion and Creating Next Generation Automatic Facial Expression Detection

在过去的二十年里，人们对自动编码面部表情的兴趣与日俱增。尽管有这种浓厚的兴趣，但计算机视觉系统在自然环境下准确编码面部表情的前景仍然难以捉摸。我们的跨学科团队将研究一种新的范式来直接解释面部对齐扭曲，而不是旨在实现它的不变性。该项目还将研究新的数据不可知特征压缩能力，以使我们能够在通过国际合作获得的世界上最大和具有挑战性的表达数据集上进行可扩展的学习。解决这两个主要的悬而未决的问题将使在自然环境中对面部表情进行准确编码成为可能。

The last two decades have seen an escalating interest in automating the coding of facial expressions. Despite this keen interest, the promise of computer vision systems to accurately code facial expressions in natural circumstances remains elusive. Our interdisciplinary team will research a new paradigm to account for facial alignment distortion directly rather than aiming to achieve invariance to it. The project will also research new data agnostic feature compaction capabilities to enable scalable learning on the world’s largest and challenging expression dataset available to us through international collaboration. Tackling these two major open problems will make accurate coding of facial expressions in natural environments achievable.