用卷积神经网络实现7类情绪实时识别，准确率超80%
Emotion Recognition Using Convolutional Neural Networks
- 基于卷积神经网络构建端到端情绪识别系统
- 在两个数据集上均达80%以上准确率，支持实时视频分析
- 适合需要实时情绪感知的交互式应用开发
情绪在日常生活中起着重要作用，有助于人们更高效地沟通与理解。面部表情可划分为7类：愤怒、厌恶、恐惧、快乐、中性、悲伤和惊讶。如何检测与识别这7种情绪在过去十年成为热门话题。本文提出一种情绪识别系统，能够通过深度学习对静态图像和实时视频进行情绪识别。系统从零开始构建，涵盖数据集收集、数据预处理、模型训练与测试全过程。给定一张图像或实时视频，系统可输出7类情绪的分类与回归结果。该系统在两个不同数据集上进行了测试，准确率超过80%。此外，实时测试结果证明了卷积神经网络在实时情绪检测中的可行性和高效性。
原文摘要 · Abstract (English)
Emotion has an important role in daily life, as it helps people better communicate with and understand each other more efficiently. Facial expressions can be classified into 7 categories: angry, disgust, fear, happy, neutral, sad and surprise. How to detect and recognize these seven emotions has become a popular topic in the past decade. In this paper, we develop an emotion recognition system that can apply emotion recognition on both still images and real-time videos by using deep learning. We build our own emotion recognition classification and regression system from scratch, which includes dataset collection, data preprocessing , model training and testing. Given a certain image or a real-time video, our system is able to show the classification and regression results for all of the 7 emotions. The proposed system is tested on 2 different datasets, and achieved an accuracy of over 80\%. Moreover, the result obtained from real-time testing proves the feasibility of implementing convolutional neural networks in real time to detect emotions accurately and efficiently.
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