Event-Level Emotion Recognition in the Wild Using Deep Facial Expression Analysis

2026-09-12

AI Summary

本文针对现实环境中面部情绪识别的挑战，提出了一种端到端的方法，通过深度卷积神经网络分析图像中的面部表情，并汇总为事件级别的整体情绪分布。

Abstract

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.

Problem

• Facial emotion recognition
• Real-world environments
• Event-level emotion recognition
• Collective emotional states

Innovation

• event-level emotion recognition
• deep convolutional neural network
• transfer learning
• EfficientNet-B2