AIOZ AI / Jan 5th / 7 min read

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

Propose new method for emotion recognition by jointly learn both the facial information and the salient information of the surrounding context.

type: research level: medium

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.