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Register a new userFacial Action Unit Detection Using Attention and Relation Learning
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Added by
Zhiwen Shao
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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Attention mechanism has recently attracted increasing attentions in the field of facial action unit (AU) detection. By finding the region of interest of each AU with the attention mechanism, AU-related local features can be captured. Most of the existing attention based AU detection works use prior knowledge to predefine fixed attentions or refine the predefined attentions within a small range, which limits their capacity to model various AUs. In this paper, we propose an end-to-end deep learning based attention and relation learning framework for AU detection with only AU labels, which has not been explored before. In particular, multi-scale features shared by each AU are learned firstly, and then both channel-wise and spatial attentions are adaptively learned to select and extract AU-related local features. Moreover, pixel-level relations for AUs are further captured to refine spatial attentions so as to extract more relevant local features. Without changing the network architecture, our framework can be easily extended for AU intensity estimation. Extensive experiments show that our framework (i) soundly outperforms the state-of-the-art methods for both AU detection and AU intensity estimation on the challenging BP4D, DISFA, FERA 2015 and BP4D+ benchmarks, (ii) can adaptively capture the correlated regions of each AU, and (iii) also works well under severe occlusions and large poses.
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Spatio-temporal relations among facial action units (AUs) convey significant information for AU detection yet have not been thoroughly exploited. The main reasons are the limited capability of current AU detection works in simultaneously learning spatial and temporal relations, and the lack of precise localization information for AU feature learning. To tackle these limitations, we propose a novel spatio-temporal relation and attention learning framework for AU detection. Specifically, we introduce a spatio-temporal graph convolutional network to capture both spatial and temporal relations from dynamic AUs, in which the AU relations are formulated as a spatio-temporal graph with adaptively learned instead of predefined edge weights. Moreover, the learning of spatio-temporal relations among AUs requires individual AU features. Considering the dynamism and shape irregularity of AUs, we propose an attention regularization method to adaptively learn regional attentions that capture highly relevant regions and suppress irrelevant regions so as to extract a complete feature for each AU. Extensive experiments show that our approach achieves substantial improvements over the state-of-the-art AU detection methods on BP4D and especially DISFA benchmarks.
This paper describes an approach to the facial action unit (AU) detection. In this work, we present our submission to the Field Affective Behavior Analysis (ABAW) 2021 competition. The proposed method uses the pre-trained JAA model as the feature extractor, and extracts global features, face alignment features and AU local features on the basis of multi-scale features. We take the AU local features as the input of the graph convolution to further consider the correlation between AU, and finally use the fused features to classify AU. The detected accuracy was evaluated by 0.5*accuracy + 0.5*F1. Our model achieves 0.674 on the challenging Aff-Wild2 database.
Facial action unit (AU) detection and face alignment are two highly correlated tasks since facial landmarks can provide precise AU locations to facilitate the extraction of meaningful local features for AU detection. Most existing AU detection works often treat face alignment as a preprocessing and handle the two tasks independently. In this paper, we propose a novel end-to-end deep learning framework for joint AU detection and face alignment, which has not been explored before. In particular, multi-scale shared features are learned firstly, and high-level features of face alignment are fed into AU detection. Moreover, to extract precise local features, we propose an adaptive attention learning module to refine the attention map of each AU adaptively. Finally, the assembled local features are integrated with face alignment features and global features for AU detection. Experiments on BP4D and DISFA benchmarks demonstrate that our framework significantly outperforms the state-of-the-art methods for AU detection.
Most existing AU detection works considering AU relationships are relying on probabilistic graphical models with manually extracted features. This paper proposes an end-to-end deep learning framework for facial AU detection with graph convolutional network (GCN) for AU relation modeling, which has not been explored before. In particular, AU related regions are extracted firstly, latent representations full of AU information are learned through an auto-encoder. Moreover, each latent representation vector is feed into GCN as a node, the connection mode of GCN is determined based on the relationships of AUs. Finally, the assembled features updated through GCN are concatenated for AU detection. Extensive experiments on BP4D and DISFA benchmarks demonstrate that our framework significantly outperforms the state-of-the-art methods for facial AU detection. The proposed framework is also validated through a series of ablation studies.
The detection of facial action units (AUs) has been studied as it has the competition due to the wide-ranging applications thereof. In this paper, we propose a novel framework for the AU detection from a single input image by grasping the textbf{c}o-textbf{o}ccurrence and textbf{m}utual textbf{ex}clusion (COMEX) as well as the intensity distribution among AUs. Our algorithm uses facial landmarks to detect the features of local AUs. The features are input to a bidirectional long short-term memory (BiLSTM) layer for learning the intensity distribution. Afterwards, the new AU feature continuously passed through a self-attention encoding layer and a continuous-state modern Hopfield layer for learning the COMEX relationships. Our experiments on the challenging BP4D and DISFA benchmarks without any external data or pre-trained models yield F1-scores of 63.7% and 61.8% respectively, which shows our proposed networks can lead to performance improvement in the AU detection task.
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