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Register a new userSpatio-Temporal Relation and Attention Learning for Facial Action Unit Detection
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Zhiwen Shao
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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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.
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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.
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.
Action Unit (AU) detection plays an important role for facial expression recognition. To the best of our knowledge, there is little research about AU analysis for micro-expressions. In this paper, we focus on AU detection in micro-expressions. Microexpression AU detection is challenging due to the small quantity of micro-expression databases, low intensity, short duration of facial muscle change, and class imbalance. In order to alleviate the problems, we propose a novel Spatio-Temporal Adaptive Pooling (STAP) network for AU detection in micro-expressions. Firstly, STAP is aggregated by a series of convolutional filters of different sizes. In this way, STAP can obtain multi-scale information on spatial and temporal domains. On the other hand, STAP contains less parameters, thus it has less computational cost and is suitable for micro-expression AU detection on very small databases. Furthermore, STAP module is designed to pool discriminative information for micro-expression AUs on spatial and temporal domains.Finally, Focal loss is employed to prevent the vast number of negatives from overwhelming the microexpression AU detector. In experiments, we firstly polish the AU annotations on three commonly used databases. We conduct intensive experiments on three micro-expression databases, and provide several baseline results on micro-expression AU detection. The results show that our proposed approach outperforms the basic Inflated inception-v1 (I3D) in terms of an average of F1- score. We also evaluate the performance of our proposed method on cross-database protocol. It demonstrates that our proposed approach is feasible for cross-database micro-expression AU detection. Importantly, the results on three micro-expression databases and cross-database protocol provide extensive baseline results for future research on micro-expression AU detection.
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.
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