论文标题
使用序列建模在野生中的表达识别
Expression Recognition in the Wild Using Sequence Modeling
论文作者
论文摘要
随着我们超越对行为的不同方面进行建模的程序，表达识别已成为人类计算机相互作用中研究的关键领域。野外表达识别是一个非常有趣的问题，并且具有挑战性，因为它涉及详细的特征提取和重型计算。本文介绍了我们为识别不同表达方式的方法和技术，即中性，愤怒，厌恶，恐惧，幸福，悲伤，在AFF-WILD2数据库中的ABAW竞争中感到惊讶。 AFF-WILD2数据库由野生标记的视频组成，该视频在框架级别上以七个不同的表达方式组成。我们通过融合音频和视觉功能来使用双模式方法，并训练基于门控复发单元（GRU）和长期记忆（LSTM）网络的序列到序列模型。我们在验证数据上显示了实验结果。所提出的方法的总体准确性为41.5 \％，比竞争基线的37 \％要好。
As we exceed upon the procedures for modelling the different aspects of behaviour, expression recognition has become a key field of research in Human Computer Interactions. Expression recognition in the wild is a very interesting problem and is challenging as it involves detailed feature extraction and heavy computation. This paper presents the methodologies and techniques used in our contribution to recognize different expressions i.e., neutral, anger, disgust, fear, happiness, sadness, surprise in ABAW competition on Aff-Wild2 database. Aff-Wild2 database consists of videos in the wild labelled for seven different expressions at frame level. We used a bi-modal approach by fusing audio and visual features and train a sequence-to-sequence model that is based on Gated Recurrent Units (GRU) and Long Short Term Memory (LSTM) network. We show experimental results on validation data. The overall accuracy of the proposed approach is 41.5 \%, which is better than the competition baseline of 37\%.
