发表机构
Kazakh-British Technical University（哈萨克-英国技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对野外事件级情绪识别难题，提出端到端流水线，结合人脸检测、深度CNN分类及概率聚合，经真实数据集验证，有效估计公共事件整体情绪分布。
AI 中文摘要
由于不受约束的成像条件，包括多张人脸、遮挡、姿态变化和复杂光照，真实环境中的面部情绪识别（FER）仍然具有挑战性。大多数现有研究侧重于个体面部情绪分类，并未解决事件层面集体情绪状态的分析问题。本文提出了一种端到端的流水线，用于从照片中进行事件级情绪识别。该方法检测每张图像中的人脸，使用深度卷积神经网络对面部表情进行分类，并聚合人脸级情绪概率以估计公共事件的整体情绪分布。在FER-2013和RAF-DB数据集上对几种CNN架构的比较评估表明，在RAF-DB上训练的EfficientNet-B2的迁移学习更适合真实世界的RGB数据。所提出的方法在一个包含1658张图像的真实世界事件数据集上进行了评估。实验结果显示，事件子集之间的情绪分布稳定，证实了事件级聚合在野外情绪分析中的有效性。
英文摘要
Facial emotion recognition (FER) in real-world environments remains challenging due to unconstrained imaging conditions, including multiple faces, occlusions, pose variations, and complex lighting. Most existing studies focus on individual facial emotion classification and do not address the analysis of collective emotional states at the event level. This paper proposes an end-to-end pipeline for event-level emotion recognition from photographs. The approach detects faces in each image, classifies facial expressions using a deep convolutional neural network, and aggregates face-level emotion probabilities to estimate the overall emotional distribution of a public event. A comparative evaluation of several CNN architectures on the FER- 2013 and RAF-DB datasets demonstrates that transfer learning with EfficientNet-B2 trained on RAF-DB is more suitable for real-world RGB data. The proposed method is evaluated on a real-world event dataset containing 1658 images. Experimental results show stable emotion distributions across event subsets, confirming the effectiveness of event-level aggregation for emotion analysis in the wild.
CommentsThe paper has been submitted to IEEE conference
Journal ref2026 IEEE 6th International Conference on Smart Information Systems and Technologies (SIST), Astana, Kazakhstan, 2026
DOI：10.1109/SIST61674.2026.11596138
