MIDAS: Mixing Ambiguous Data With Soft Labels for Dynamic Facial Expression Recognition

概要

Dynamic facial expression recognition (DFER) is an important task in the field of computer vision. To apply automatic DFER in practice, it is necessary to accurately recognize ambiguous facial expressions, which often appear in data in the wild. In this paper, we propose MIDAS, a data augmentation method for DFER, which augments ambiguous facial expression data with soft labels consisting of probabilities for multiple emotion classes. In MIDAS, the training data are augmented by convexly combining pairs of video frames and their corresponding emotion class labels, which can also be regarded as an extension of mixup to soft- labeled video data. This simple extension is remarkably effective in DFER with ambiguous facial expression data. To evaluate MIDAS, we conducted experiments on the DFEW dataset. The results demonstrate that the model trained on the data augmented by MIDAS outperforms the existing state-of-the-art method trained on the original dataset.

早志英朗

准教授

深層学習やベイズ推定を基盤とした機械学習アルゴリズムの開発を中心に、生体信号解析、医用画像処理などの応用研究に従事。

武村紀子

招へい准教授

パターン認識、機械学習等を用いた環境知能や歩容認証等に関する研究に従事。

長原一

教授

コンピューテーショナルフォトグラフィ、コンピュータビジョンを専門とし実世界センシングや情報処理技術、画像認識技術の研究を行う。さらに、画像センシングにとどまらず様々なセンサに拡張したコンピュテーショナルセンシング手法の開発や高次元で冗長な実世界ビッグデータから意味のある情報を計測するスパースセンシングへの転換を目指す。