Source-linked AI summary
Context-Aware Emotion Recognition Networks
Jiyoung Lee, Seungryong Kim, Sunok Kim, Jungin Park, Kwanghoon Sohn
TL;DR
Facial-only emotion recognition has limited access to contextual information, especially when facial signals are ambiguous. CAER-Net jointly encodes facial and attentive context regions through two streams and adaptive fusion, while introducing the CAER benchmark. The method outperforms baseline networks across several benchmarks, although its performance could be further boosted by incorporating Fan et al.'s method.
Problem
Facial-only emotion recognition provides limited context representation, and ambiguous facial signals can reduce recognition performance.
Method
CAER-Net separately encodes face and context regions, hides faces to guide attention toward relevant context, and adaptively fuses the resulting features.
Results
CAER-Net outperforms baseline networks on AFEW and CAER, with both face and context producing the best CAER result and a 3.53 % improvement over CAER-Net-S.
Takeaways & Limitations
The results support using context alongside facial expression for context-aware emotion recognition and provide the CAER benchmark for this setting.
Takeaways & Limitations
The authors leave incorporating Fan et al.'s method as future work because it could further boost CAER-Net's performance.
Abstract
from arXiv · showhide
Traditional techniques for emotion recognition have focused on the facial expression analysis only, thus providing limited ability to encode context that comprehensively represents the emotional responses. We present deep networks for context-aware emotion recognition, called CAER-Net, that exploit not only human facial expression but also context information in a joint and boosting manner. The key idea is to hide human faces in a visual scene and seek other contexts based on an attention mechanism. Our networks consist of two sub-networks, including two-stream encoding networks to seperately extract the features of face and context regions, and adaptive fusion networks to fuse such features in an adaptive fashion. We also introduce a novel benchmark for context-aware emotion recognition, called CAER, that is more appropriate than existing benchmarks both qualitatively and quantitatively. On several benchmarks, CAER-Net proves the effect of context for emotion recognition. Our dataset is available at http://caer-dataset.github.io.
1. Introduction
Existing emotion-recognition methods largely rely on facial expressions, but ambiguous faces and limited context-focused datasets constrain recognition in natural settings. CAER-Net addresses this by jointly using facial and attentive scene-context information, alongside the CAER video benchmark.
- Facial-expression methods and datasets such as AFEW and FER2013 primarily represent cropped, aligned faces.
- Ambiguous facial signals can cause conventional methods to fail, whereas surrounding actions, interactions, and places provide additional emotional information.
- Existing context-aware approaches consider clues such as gesture, place, or holistic appearance but lack practical extraction of salient context.
- CAER-Net jointly exploits facial expression and scene context using separate face and context streams, attention-based face hiding, and adaptive feature fusion.
- The CAER benchmark contains many TV-show video clips with annotated emotion categories, and CAER-Net outperforms baseline networks on AFEW and CAER.
2. Related Work
Related work has expanded from facial-expression analysis toward visual clues, attention, and naturalistic datasets. However, prior approaches remain limited in extracting salient context for human-centric emotion recognition.
- Emotion recognition approaches: Earlier emotion-recognition systems used facial action coding, handcrafted features, shallow classifiers, and later deep CNNs with temporal modeling.
- Emotion recognition approaches: Other visual-clue methods use shoulders, body pose, detected events, objects, scenes, or annotated body boxes to recognize emotions.
- Emotion recognition approaches: These context-based approaches lack practical solutions for extracting salient context information for context-aware emotion recognition.
- Emotion recognition datasets: Emotion datasets range from laboratory-controlled expression recordings to naturalistic videos and static subsets derived from movie and television content.
- Attention inference: Attention models identify discriminative regions through mined regions, activation maps, or dedicated attention modules.
- Attention inference: Visual-sentiment attention methods produce image-level sentiment maps rather than addressing human-centric emotion recognition.
3. Proposed Method
CAER-Net combines separate face and context encoding with attention-guided context selection and adaptive fusion for emotion recognition in images and videos.
- Context Encoding: The context stream hides facial regions and uses an attention inference module to localize salient contextual regions.Attention is learned implicitly and applied to context features through element-wise multiplication.
- Two-stream Architecture: CAER-Net uses two-stream encoding networks to separately extract facial-expression and context features before adaptive fusion.The architecture includes face and context streams, followed by an adaptive fusion network.
- Dynamic and Static Models: Dynamic and static variants support video and image emotion recognition, respectively, using 3D and 2D convolutional and pooling layers.The dynamic model exploits temporal connectivity, while the static model processes a single frame and face-hidden image.
- Face Encoding: The face stream processes temporally stacked face-cropped inputs with 3D-CNN operations to represent spatiotemporal facial-expression features.The first pooling layer avoids merging temporal signals too early.
- Adaptive Fusion: Adaptive fusion learns separate attention weights for facial and contextual features instead of directly concatenating them.The weighted features are concatenated and passed to a classifier for the final emotion category.
- CAER Benchmark: The CAER benchmark is built by segmenting videos into shots and removing face-undetected, group-level, and ambiguous shots before emotion annotation.The procedure is illustrated in Figure 5.
4. The CAER Benchmark
CAER is introduced as a context-aware benchmark built from large-scale TV-show video clips, addressing the limitations of facial-analysis datasets. It provides annotated dynamic and static data with surrounding visual context for emotion recognition.
- CAER was created because many existing emotion datasets focus on facial analysis and omit surrounding context, while EMOTIC lacks human facial images.These differences make existing benchmarks less suitable for context-aware emotion recognition.
- The dataset was collected from video clips spanning 79 TV shows and manually annotated across seven emotion categories, including neutral.Six annotators labeled 20,484 initial clips while listening to their audio tracks.
- Table 1 reports the number of video clips assigned to each emotion category in the CAER dataset.The table organizes the benchmark by category-specific clip counts.
- After filtering low-confidence samples, CAER contained 13,201 clips and about 1.1M frames, with sequence lengths ranging from around 30 to more than 120 frames.The average sequence length was 90 frames.
- CAER-S was constructed as a static image subset containing about 70K images extracted from CAER.The dataset was randomly split into training, validation, and testing sets at 70%, 10%, and 20%, respectively.
5. Experiments
Experiments evaluate CAER-Net and its components on CAER, CAER-S, and AFEW, showing gains from joint face-context modeling, attention, and temporal inputs.
- 3.53 % improvement over CAER-Net-S shows that the dynamic CAER-Net benefits from temporal inputs for context-aware emotion recognition.
- 74.13 % accuracy is achieved by the face-only CAER-Net w/F baseline, while using both face and context gives the best ablation result.
- 7.48% and 5.65% increases in happy and neutral accuracies indicate that context helps distinguish these categories beyond facial expression alone.
- CAER-Net-S improves classification performance over fine-tuned ResNet by 5.05% on CAER-S and outperforms baseline networks across categories.
- Attention maps show that the context stream localizes salient scene information rather than only facial expressions, supporting context-aware recognition.
- Combining CAER with AFEW produces a highly improved result, indicating that CAER complements AFEW’s data distribution.
- More complicated face-encoding networks could further boost CAER-Net performance, which the authors reserve for future work.
6. Conclusion
The paper presents CAER-Net for jointly using facial expression and context, together with the CAER benchmark for context-aware emotion recognition.
- CAER-Net seeks salient context by hiding facial regions and combines contextual and facial information for emotion estimation.
- The CAER benchmark is introduced as more appropriate for context-aware emotion recognition than existing benchmarks qualitatively and quantitatively.