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Global-Local Attention for Context-aware Emotion Recognition (Part 2)

In this part, we will conduct experiements for validating the effectiveness of our proposed Global-Local Attention for Context-aware Emotion Recognition. Here, we only focus on static images with background context as our input. Therefore, we choose the static CAER (CAER-S) dataset [2] to validate our method. However, while experimenting with the CAER-S dataset, we observe that there is a correlation between images in the training and the test sets, which can make the model less robust to changes in data and may not generalize well on unseen samples. More specifically, many images in the training and the test set of the CAER-S dataset are extracted from the same video, hence making them look very similar to each other. To properly evaluate the models, we propose a new way to extract static frames from the CAER video clips to create a new static image dataset called Novel CAER-S (NCAER-S). In particular, for each video in the original CAER dataset, we split the video into multiple parts. Then we randomly select one frame of each part to include in the new NCAER-S dataset. Any original video that provides frames for the training set will be removed from the testing set. This process assures the new dataset is novel while the training frames and testing frames are never from one original input video.

#Context-aware emotion recognition results

Table 1. Comparison with recent methods on the CAER-S dataset.

Table 1 summarizes the results of our network and other recent state-of-the-art methods on the CAER-S dataset [2]. This table clearly shows that integrating our GLA module can significantly improve the accuracy performance of the recent CAER-Net. In particular, our GLAMOR-Net (original) achieves 77.90% accuracy, which is a +4.38% improvement over the CAER-Net-S. When compared with other recent state-of-the-art approaches, the table clearly demonstrates that our GLAMOR-Net (ResNet-18) outperforms all those methods and achieves a new state-of-the-art performance with an accuracy of 89.88%. This result confirms our global-local attention mechanism can effectively encode both facial information and context information to improve the human emotion classification results.

#Component Analysis

To further analyze the contribution of each component in our proposed method, we experiment with 4 different input settings on the NCAER-S dataset: (i) face only, (ii) context only with the facial region being masked, (iii) context only with the facial region visible, and (iv) both face and context (with masked face). When the context information is used, we compare the performance of the model with different context attention approaches. Note that to compute the saliency map with the proposed GLA in the (ii) and (iii) setting, we extract facial features using the Facial Encoding Module, however, these features are only used as the input of the GLA module to guide the context attention map learning process and not as the input of the Fusion Network to predict the emotion category. The results of these settings are summarized in Table 2.

Table 2. Ablation study of our proposed method on the NCAER-S dataset. w/F, w/mC, w/fC, w/CA, w/GLA denote using the output of the Facial Encoding Module, the Context Encoding Module with masked faces as input, the Context Encoding Module with visible faces as input, the standard attention in [2] and our proposed GLA, respectively, as input to the Fusion Network.

The results clearly show that our GLA consistently helps improve performance in all settings. Specifically, in setting (ii), using our GLA achieves an improvement of 1.06\% over method without attention. Our GLA also improves the performance of the model when both facial and context information is used to predict emotion. Specifically, our model with GLA achieves the best result with an accuracy of 46.91\%, which is higher than the method with no attention 3.72\%. The results from Table 2 show the effectiveness of our Global-Local Attention module for the task of emotion recognition. They also verify that the use of both the local face region and global context information is essential for improving emotion recognition accuracy.

#Qualitative Analysis

Figure 5 shows the qualitative visualization with learned attention maps obtained by our method GLAMOR-Net in comparison with CAER-Net-S. It can be seen that our Global-Local Attention mechanism produces better saliency maps and helps the model attend to the right discriminative regions in the surrounding background than the attention map produced by CAER-Net-S. As we can see, our model is able to focus on the gesture of the person (Figure 5f) and also the face of surrounding people (Figure 5c and 5d) to infer the emotion accurately.

Figure 5.Visualization of the attention maps. From top to bottom: original image in the NCAER-S dataset, image with masked face, attention map of the CAER-Net-S, and attention map of our GLAMOR-Net.

Figure 6 shows some emotion recognition results of different approaches on the CAER-S dataset. More specifically, the first two rows (i) and (ii) contain predictions of the CAER-Net-S while the last two rows (iii) and (iv) show the results of our GLAMOR-Net. In some cases, our model was able to exploit the context effectively to perform inference accurately. For instance, with the same sad image input (shown on the (i) and (iii) rows), the CAER-Net-S misclassified it as neutral while the GLAMOR-Net correctly recognized the true emotion category. It might be because our model was able to identify that the man was hugging and appeasing the woman and inferred that they were sad. Another example is shown on the (i) and (iii) rows of the fear column. Our model classified the input accurately, while the CAER-Net-S is confused between the facial expression and the wedding surrounding, thus incorrectly predicted the emotion as happy.

Figure 5. Example predictions on the test set. The first two rows (i) and (ii) show the results of the CAER-Net-S while the last two rows (iii) and (iv) demonstrate predictions of our GLAMOR-Net. The columns names from (a) to (g) denote the ground-truth emotion of the images.

#Conclusion

We have presented a novel method to exploit context information more efficiently by using the proposed globallocal attention model. We have shown that our approach can noticeably improve the emotion classification accuracy compared to the current state-of-the-art results in the context-aware emotion recognition task. The results on the context-aware emotion recognition datasets consistently demonstrate the effectiveness and robustness of our method.

#References

[1] Chorowski, J., Bahdanau, D., Serdyuk, D., Cho, K., Bengio, Y.: Attention-based models for speech recognition. In NIPS, 2015.

[2] Lee, J., Kim, S., Kim, S., Park, J., Sohn, K.: Context-aware emotion recognition networks. In ICCV, 2019.

Global-Local Attention for Context-aware Emotion Recognition (Part 1)

Automatic emotion recognition has been a longstanding problem in both academia and industry. It enables a wide range of applications in various domains, ranging from healthcare, surveillance to robotics and human-computer interaction. Recently, significant progress has been made in the field and many methods have demonstrated promising results. However, recent works mainly focus on facial regions while ignoring the surrounding context, which is shown to play an important role in the understanding of the perceived emotion, especially when the emotions on the face are ambiguous or weakly expressed (see the examples in Figure 1).

Figure 1. Facial expression information is not always sufficient to infer people's emotions, especially when facial regions can not be seen clearly or are occluded.

We hypothesize that the local information (i.e., facial region) and global information (i.e., context background) have a correlative relationship, and by simultaneously learning the attention using both of them, the accuracy of the network can be improved. This is based on the fact that the emotion of one person can be indicated by not only the face’s emotion (i.e., local information) but also other context information such as the gesture, pose, or emotion/pose of a nearby person. To that end, we propose a new deep network, namely, Global-Local Attention for Emotion Recognition Network (GLAMOR-Net), to effectively recognize human emotions using a novel global-local attention mechanism. Our network is designed to extract features from both facial and context regions independently, then learn them together using the attention module. In this way, both the facial and contextual information is used altogether to infer human emotions.

#Overview

Figure 2. The architecture of our proposed network. The whole process includes three steps. We extract the facial information (local) and context information (global) using two Encoding Modules. We then perform attention inference on the global context using the Global-Local Attention mechanism. Lastly, we fuse both features to determine the emotion.
Figure 2 shows an overview of our method. Specifically, we assume that emotions can be recognized by understanding the context components of the scene together with the facial expression. Our method aims to do emotion recognition in the wild by incorporating both facial information of the person’s face and contextual information surrounding that person. Our model consists of three components: Encoding Module, Global-Local Attention (GLA) Module, and Fusion Module. Our key design is the novel GLA module, which utilizes facial features as the local information to attend better to salient locations in the global context.

#Face and Context Encoding

Our Encoding Module comprises the Facial Encoding Module to learn the face-specific features, and the Context Encoding Module to learn the context-specific features. Specifically, both the Face Encoding and Context Enconding Module are built on several convolutional layers to extract meaningful features from the corresponding input. Each module is comprised of five convolutional layers followed by a Batch Normalization layer an ReLU activation function. The number of filters starts with 32 in the first layer, increasing by a factor of 2 at each subsequent layer except the last one. Our network ends up with 256-channel feature map, which is the embedded representation with respect to the input image. In practice, we also mask the facial regions in the raw input to prevent the attention module from only focusing on the facial region while omitting the context information in other parts of the image.

#Global-Local Attention

Inspired by the attention mechanism [1], to model the associative relationship of the local information (i.e., the facial region in our work) and global information (i.e., the surrounding context background), we propose the Global-Local Attention Module to guide the network focus on meaningful regions (Figure 3). In particular, our attention mechanism models the hidden correlation between the face and different regions in the context by capturing their similarity.

Figure 3. The proposed Global-Local Attention module takes the extracted face feature vector and the context feature map as the input to perform context attention inference.

We first reduce the facial feature map $\mathbf{F}_f$ into vector representation using the Global Pooling operator, denoted as $\mathbf{v}_f$. The context feature map can be viewed as a set of $W_c \times H_c$ vectors with $D_c$ dimensions, each vector in each cell $(i,j)$ represents the embedded features at that location with the corresponding patch in the input image. Therefore, at each region $(i,j)$ in the context feature map, we have $\mathbf{F}_c^{(i,j)} = \mathbf{v}_{i,j}$.

We then concatenate $[\mathbf{v}_f; \mathbf{v}_{i,j}]$ into a holistic vector $\bar{\mathbf{v}}_{i,j}$, which contains both information about the face and some small regions of the scene. We then employ a feed-forward neural network to compute the score corresponding to that region by feeding $\bar{\mathbf{v}}_{i,j}$ into the network. By applying the same process for all regions, each region $(i,j)$ will output a raw score value $s_{i,j}$, we spatially apply the Softmax function to produce the attention map $a_{i,j} = \text{Softmax}(s_{i,j})$. To obtain the final context representation vector, we squish the feature maps by taking the average over all the regions weighted by $a_{i,j}$ as follow:

$\mathbf{v}_c = \Sigma_i\Sigma_j(a_{i,j} \odot \mathbf{v}_{i,j})$

where $\mathbf{v}_c \in \mathbb{R}^{D_c}$ is the final single vector encoding the context information Intuively, $\mathbf{v}_c$ mainly contains information from regions that have high attention, while other nonessential parts of the context are mostly ignored. With this design, our attention module can guide the network focus on important areas based on both facial information and context information of the image.

#Face and Context Fusion

Figure 4. Detailed illustration of the Adaptive Fusion.

The Fusion Module takes the face $\mathbf{v}_f$ and the context reprsentation $\mathbf{v}_c$ as inputs, then the face score and context score are produced separately by two neural networks:

$s_f = \mathcal{F}(\mathbf{v}_f; \phi_f), \quad\quad s_c = \mathcal{F}(\mathbf{v}_c; \phi_c)$

where $\phi_f$ and $\phi_c$ are the network parameters of the face branch and context branch, respectively. Next, we normalize these scores by the Softmax function to produce weights for each face and context branch

$w_f = \frac{\exp(s_f)}{\exp(s_f)+\exp(s_c)}, \quad w_c = \frac{\exp(s_c)}{\exp(s_f)+\exp(s_c)}$

In this way, we let the two networks competitively determine which branch is more useful than the other. Then we amplify the more useful branch and lower the effect of the other by multiplying the extracted features with the corresponding weight:

$\mathbf{v}_f \leftarrow \mathbf{v}_f \odot w_f , \quad\quad \mathbf{v}_c \leftarrow \mathbf{v}_c \odot w_c$

Finally, we use these vectors to estimate the emotion category. Specifically, in our experiments, after multiplying both $\mathbf{v}_f$ and $\mathbf{v}_c$ by their corresponding weights, we concatenate them together as the input for a network to make final predictions. Figure 4 shows our fusion procedure in detail.

#References

[1] Chorowski, J., Bahdanau, D., Serdyuk, D., Cho, K., Bengio, Y.: Attention-based models for speech recognition. In NIPS, 2015.

[2] Lee, J., Kim, S., Kim, S., Park, J., Sohn, K.: Context-aware emotion recognition networks. In ICCV, 2019.

Uncertainty-aware Label Distribution Learning for Facial Expression Recognition (Part 1)

Facial expression recognition (FER) plays an important role in understanding people's feelings and interactions between humans. Recently, automatic emotion recognition has gained a lot of attention from the research community due to its tremendous applications in education, healthcare, human analysis, surveillance or human-robot interaction. Recent FER methods are mostly based on deep learning and can achieve impressive results. The success of deep models can be attributed to large-scale FER datasets [1][2]. However, ambiguities of facial expression is still a key challenge in FER. Specifically, people with different backgrounds might perceive and interpret facial expressions differently, which can lead to noisy and inconsistent annotations. In addition, real-life facial expressions usually manifest a mixture of feelings rather than only a single emotion.

#Motivation and Proposed Solution

Figure 1. Examples of real-world ambiguous facial expressions that can lead to noisy and inconsistent annotation.

As an example, Figure 1 shows that people may have different opinions about the expressed emotion, particularly in ambiguous images. Consequently, a distribution over emotion categories is better than a single label because it takes all sentiment classes into account and can cover various interpretations, thus mitigating the effect of ambiguity. However, existing large-scale FER datasets only provide a single label for each sample instead of a label distribution, which means we do not have a comprehensive description for each facial expression. This can lead to insufficient supervision during training and pose a big challenge for many FER systems.

To overcome the ambiguity problem in FER, we proposes a new uncertainty-aware label distribution learning method that constructs emotion distributions for training samples. Specifically, we leverage the neighborhood information of samples that have similar expressions to construct the emotion distributions from single labels and utilize them as training supervision signal.

#Methodology

#Preliminaries

We denote $\mathbf{x} \in \mathcal{X}$ as the instance variable in the input space $\mathcal{X}$ and $\mathbf{x}^{i}$ as the particular $i$-th instance. The label set is denoted as $\mathcal{Y} = \{y_1, y_2,..., y_m\}$ where $m$ is the number of classes and $y_j$ is the label value of the $j$-th class. The logical label vector of $\mathbf{x}^{i}$ is indicated by $\mathbf{l}^{i}$ = $(l^{i}_{y_1}, l^{i}_{y_2}, ..., l^{i}_{y_m})$ with $\mathbf{l}^{i}_{y_j} \in \{0, 1\}$ and $\| \mathbf{l} \| _1 = 1$. We define the label distribution of $\mathbf{x}^{i}$ as $\mathbf{d}^{i}$ = $(d^{i}_{y_1}, d^{i}_{y_2}, ..., d^{i}_{y_m})$ with $\| \mathbf{d} \| _1 = 1$ and $d^{i}_{y_j} \in [0, 1]$ representing the relative degree that $\mathbf{x}^{i}$ belongs to the class $y_j$.

Most existing FER datasets assign only a single class or equivalently, a logical label $\mathbf{l}^{i}$ for each training sample $\mathbf{x}^{i}$. In particular, the given training dataset is a collection of $n$ samples with logical labels $D_l$ = $\{ (\mathbf{x}^{i}, \mathbf{l}^{i}) \vert 1 \le i \le n\}$. However, we find that a label distribution $\mathbf{d}^i$ is a more comprehensive and suitable annotation for the image than a single label.

Inspired by the recent success of label distribution learning (LDL) in addressing label ambiguity [3], we aim to construct an emotion distribution $\mathbf{d}^i$ for each training sample $\mathbf{x}^i$, thus transform the training set $D_l$ into $D_d$ = $\{ (\mathbf{x}^{i}, \mathbf{d}^{i}) \vert 1 \le i \le n\}$, which can provide richer supervision information and help mitigate the ambiguity issue. We use cross-entropy to measure the discrepancy between the model's prediction and the constructed target distribution. Hence, the model can be trained by minimizing the following classification loss:

$\mathcal{L}_{cls} = \sum_{i=1}^n \text{CE}\left(\mathbf{d}^i, f(\mathbf{x}^i; \theta)\right) = -\sum_{i=1}^n \sum_{j=1}^m \mathbf{d}_j^{i} \log f_j(\mathbf{x}^{i};\theta).$

where $f(\mathbf{x}; \theta)$ is a neural network with parameters $\theta$ followed by a softmax layer to map the input image $\mathbf{x}$ into a emotion distribution.

#Overview

Figure 2. An overview of our Label Distribution Learning with Valence-Arousal (LDLVA) for facial expression recognition under ambiguity.

An overview of our method is presented in Figure 2. To construct the label distribution for each training instance $\mathbf{x}^i$, we leverage its neighborhood information in the valence-arousal space. Particularly, we identify $K$ neighbor instances for each training sample $\mathbf{x}^i$ and utilize our adaptive similarity mechanism to determine their contribution degrees to the target distribution $\mathbf{d}^i$. Then, we combine the neighbors' predictions and their corresponding contribution degrees with the provided label $\mathbf{l}^i$ and $\mathbf{l}^i$'s uncertainty factor to obtain the label distribution $\mathbf{d}^i$. The constructed distribution $\mathbf{d}^i$ will be used as supervision information to train the model via label distribution learning.

We assume that the label distribution of the main instance $\mathbf{x}^i$ can be computed as a linear combination of its neighbors' distributions. To determine the contribution of each neighbor, we propose an adaptive similarity mechanism that not only leverages the relationships between $\mathbf{x}^i$ and its neighbors in the auxiliary space but also utilizes their feature vectors extracted from the backbone. We choose the valence-arousal [4] as the auxiliary space to construct the target label distribution. We use the $K$-Nearest Neighbor algorithm to identify $K$ closest points for each training sample $\mathbf{x}^i$, denoted as $N(i)$. We calculate the adaptive contribution degrees of neighbor instances as the product of the local similarity $s^i_k$ and the calibration score $\zeta^i_k$ as follows:

$c^i_k = \begin{cases} \zeta^i_k s^i_k, &\text{for } \mathbf{x}^k \in N(i), \\ 0, &\text{otherwise}. \end{cases}$

where the local similarity $s^i_k$ is defined based on the distance between the instance and its neighbor in the valence-arousal space $\mathbf{a}^i$ and $\mathbf{a}^k$

$s^i_k = \exp\left(-\frac{\| \mathbf{a}^i - \mathbf{a}^k \|^2_2}{\delta^2}\right), \quad \forall \mathbf{x}^k \in N(i)$

We utilize a multilayer perceptron (MLP) $g$ with parameter $\phi$ to calculate the adaptive calibration score from the extracted features of the two instances $\mathbf{v}^i$ and $\mathbf{v}^k$ obtained from the backbone.

$\zeta^i_k = Sigmoid\left(g([\mathbf{v}^i,\mathbf{v}^{k}];\phi)\right)$

The proposed adaptive similarity can correct the similarity errors in the valance-arousal space, as the valence-arousal values are not always available in practice and we leverage an existing method to generate pseudo-valence-arousal.

#Uncertainty-aware Label Distribution Construction

After obtaining the contribution degree of each neighbor $\mathbf{x}^k \in N(i)$, we can now generate the target label distribution $\mathbf{d}^i$ for the main instance $\mathbf{x}^i$. The target label distribution is calculated using the logical label $\mathbf{l}^i$ and the aggregated distribution $\tilde{\mathbf{d}}^i$ defined as follows:

$\tilde{\mathbf{d}^i} = \frac{\sum_k c^i_k f(\mathbf{x}^{k};\theta)}{\sum_k c^i_k}, \\ \mathbf{d}^i = (1-\lambda^i) \mathbf{l}^i + \lambda^i \tilde{\mathbf{d}^i}$

where $\lambda^i \in [0,1]$ is the uncertainty factor for the logical label. It controls the balance between the provided label $\mathbf{l}^i$ and the aggregated distribution $\tilde{\mathbf{d}^i}$ from the local neighborhood.

Intuitively, a high value of $\lambda^i$ indicates that the logical label is highly uncertain, which can be caused by ambiguous expression or low-quality input images, thus we should put more weight towards neighborhood information $\tilde{\mathbf{d}^i}$. Conversely, when $\lambda^i$ is small, the label distribution $\mathbf{d}^i$ should be close to $\mathbf{l}^i$ since we are certain about the provided manual label. In our implementation, $\lambda^i$ is a trainable parameter for each instance and will be optimized jointly with the model's parameters using gradient descent.

#Loss Function

To enhance the model's ability to discriminate between ambiguous emotions, we also propose a discriminative loss to reduce the intra-class variations of the learned facial representations. We incorporate the label uncertainty factor $\lambda^i$ to adaptively penalize the distance between the sample and its corresponding class center. For instances with high uncertainty, the network can effectively tolerate their features in the optimization process. Furthermore, we also add pairwise distances between class centers to encourage large margins between different classes, thus enhancing the discriminative power. Our discriminative loss is calculated as follows:

$\mathcal{L}_D = \frac{1}{2}\sum_{i=1}^n (1-\lambda^i)\Vert \mathbf{v}^i - \mathbf{\mu}_{y^i} \Vert_2^2 + \sum_{j=1}^m \sum_{\substack{k=1 \\ k \neq j}}^m \exp \left(-\frac{\Vert\mathbf{\mu}_{j}-\mathbf{\mu}_{k}\Vert_2^2}{\sqrt{V}}\right)$

where $y^i$ is the class index of the $i$-th sample while $\mathbf{\mu}_{j}$, $\mathbf{\mu}_{k}$, and $\mathbf{\mu}_{y^i}$ $\in \mathbb{R}^V$ are the center vectors of the ${j}$-th, ${k}$-th, and $y^i$-th classes, respectively. Intuitively, the first term of $\mathcal{L}_D$ encourages the feature vectors of one class to be close to their corresponding center while the second term improves the inter-class discrimination by pushing the cluster centers far away from each other. Finally, the total loss for training is computed as:

$\mathcal{L} = \mathcal{L}_{cls} + \gamma\mathcal{L}_D$

where $\gamma$ is the balancing coefficient between the two losses.

#References

[1] Ali Mollahosseini, Behzad Hasani, and Mohammad H. Mahoor. Affectnet: A database for facial expression, valence, and arousal computing in the wild. IEEE Transactions on Affective Computing, 2019

[2] Shan Li, Weihong Deng, and JunPing Du. Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In CVPR, 2017.

[3] B. Gao, C. Xing, C. Xie, J. Wu, and X. Geng. Deep label distribution learning with label ambiguity. IEEE Transactions on Image Processing, 2017.

Uncertainty-aware Label Distribution Learning for Facial Expression Recognition (Part 2)

In the previous post, we have introduced the our proposed method for Facial Expression Recognition. In this post, we will examine the effectiveness and efficiency of the proposal.

#Experimental Results

#Noisy and Inconsistent Labels

Table 1. Test performance with synthetic noise.

We conduct experiments to study the robustness of our LDLVA on mislabelled data by adding synthetic noise to AffectNet, RAF-DB, and SFEW datasets. Specifically, we randomly flip the manual labels to one of the other categories. . We report the mean accuracy and standard error in Table 1. The results clearly show that our method consistently outperforms other approaches in all cases. We also observe that the improvements are even more apparent when the noise ratio increases, for example, the accuracy improvement on RAF-DB is 4.7\% with 10\% noise and 6.93\% with 30\% noise. The consistent results under various settings demonstrate the ability of our method to effectively deal with noisy annotation, which is crucial in the robustness against label ambiguity.

Table 2. Test performance with inconsistent labels between cross-datasets.

Since the annotations for large-scale FER data are commonly obtained via crowd-sourcing, this can create label inconsistency, especially between different datasets. To examine the effectiveness of our proposed methods in dealing with this problem, we also perform experiment with the cross-dataset protocol. Table 2 shows that our method achieves the best performance on all three datasets and the highest average accuracy and surpasses the current state-of-the-art methods. This confirms the advantages of our method over previous works and demonstrates the generalization ability to data with label inconsistency, which is essential for real-world FER applications.

#Comparison with state of the arts

Table 3. Comparison with recent methods on the original datasets.

We further compare our method with several state of the arts on the original AffectNet, RAF-DB, and SFEW to evaluate the robustness of our method to the uncertainty and ambiguity that unavoidably exists in real-world FER datasets. The results are presented in Table 3. By leveraging label distribution learning on valence-arousal space, our model outperforms other methods and achieves state-of-the-art performance on AffectNet, RAF-DB, and SFEW. Although these datasets are considered to be "clean", the results suggest that they indeed suffer from uncertainty and ambiguity.

#Qualitative Analysis

Real-world Ambiguity: To understand more about real-world ambiguous expressions, we conducted a user study in which we asked participants to choose the most clearly expressed emotion on random test images. We compare our model's predictions with the survey results in Figure 3. We can see that these images are ambiguous as they express a combination of different emotions, hence the participants do not fully agree and have different opinions about the most prominent emotion on the faces. It is further shown that our model can give consistent results and agree with the perception of humans to some degree.

Figure 3. Comparison of the results from our user study and our model.

Uncertainty Factor: Figure 4 shows the estimated uncertainty factors of some training images and their original labels. The uncertainty values decrease from top to bottom. Highly uncertain labels can be caused by low-quality inputs (as shown in Angry and Surprise columns) or ambiguous facial expressions. In contrast, when the emotions can be easily recognized as those in the last row, the uncertainty factors are assigned low values. This characteristic can guide the model to decide whether to put more weight on the provided label or the neighborhood information. Therefore, the model can be more robust against uncertainty and ambiguity.

Figure 4. Visualization of uncertainty values of some examples from RAF-DB dataset.

#Conclusion

We have introduced a new label distribution learning method for facial expression recognition by leveraging structure information in the valence-arousal space to recover the intensities distributed over emotion categories. The constructed label distribution provides rich information about the emotions, thus can effectively describe the ambiguity degree of the facial image. Intensive experiments on popular datasets demonstrate the effectiveness of our method over previous approaches under inconsistency and uncertainty conditions in facial expression recognition.

#References

[1] Ali Mollahosseini, Behzad Hasani, and Mohammad H. Mahoor. Affectnet: A database for facial expression, valence, and arousal computing in the wild. IEEE Transactions on Affective Computing, 2019

[2] Shan Li, Weihong Deng, and JunPing Du. Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In CVPR, 2017.

[3] B. Gao, C. Xing, C. Xie, J. Wu, and X. Geng. Deep label distribution learning with label ambiguity. IEEE Transactions on Image Processing, 2017.