Off-TANet:a Lightweight Neural Micro-Expression Recognizer with Optical Flow Features and Integrated Attention Mechanism

EasyChair Preprint 6909

Abstract

Micro-expression recognition is a video sentiment classification task with extremely small sample size. The transience and spatial locality of micro-expressions bring difficulties to constructing large micro-expression databases and designing micro-expression recognition algorithms. In order to reach the balance between classification accuracy and model complexity in this domain, we propose a lightweight neural micro-expression recognizer, Off-TANet, which is based on apex-onset optical flow features. The neural network contains a simple yet powerful triplet attention mechanism, and the powerfulness of this design could be interpreted in 2 aspects, FACS AU and matrix sparseness. The model evaluation is conducted with a LOSO cross-validation strategy on a combined database including 3 mainstream micro-expression databases. With obviously fewer total parameters (59,403) , the results of the experiment indicate that the model achieves an average recall of 0.7315 and an average F1-score of 0.7242, exceeding other major architectures in this domain. A series of ablation experiments are also conducted to ensure the validity of our model design.

Keyphrases: Convolutional Neural Networks, Micro-expression recognition, Optical flow features, attention module, self-attention mechanism

@booklet{EasyChair:6909,
  author    = {Jiahao Zhang and Feng Liu and Aimin Zhou},
  title     = {Off-TANet:a Lightweight Neural Micro-Expression Recognizer with Optical Flow Features and Integrated Attention Mechanism},
  howpublished = {EasyChair Preprint 6909},
  year      = {EasyChair, 2021}}