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February 10, 2020

Guidelines:

Pose-invariant, subject-independent, expression-discriminative representation learning

Experiments:

  1. baseline: directly regress 3DMM expression coefficients

  2. multi-task learning: jointly regress 3DMM expression coefficients and 2D facial landmarks

Optimization:

3DMM coefficients adaptively weighted, triplet loss/ contrastive loss, face recognition

Choose the appropriate architecture (ResNet 50 / face analysis network / which layers to share) and hyper-parameters to demonstrate the effectiveness of Multi-Task Learning, i.e. each component contributes to the overall performance.

Goal:

ACM Multimedia Conference 2020

  • Regular Papers Submission (Abstract) 21 Mar 2020

  • Regular Papers Submission 28 Mar 2020