论文标题
带有卷积神经网络和同型的实时面部表情表情表情掩盖
Real-Time Facial Expression Emoji Masking with Convolutional Neural Networks and Homography
论文作者
论文摘要
基于神经网络的算法在许多应用程序中都表现出成功。在图像处理中，可以训练卷积神经网络（CNN）以对人脸的图像进行分类。在这项工作中，我们创建了一个系统，该系统用各自情感的表情符号掩盖了学生的脸。我们的系统由三个构件组成：使用梯度的直方图（HOG）和支持向量机（SVM），面部表达分类的面部检测，使用在FER2013数据集中训练的CNN进行分类，最后将各个Emoji通过同型估计掩盖到学生的面部。 （演示：https：//youtu.be/gcjtxw1y8pw）我们的结果表明，该管道可实时部署，并且在教育环境中可用。
Neural network based algorithms has shown success in many applications. In image processing, Convolutional Neural Networks (CNN) can be trained to categorize facial expressions of images of human faces. In this work, we create a system that masks a student's face with a emoji of the respective emotion. Our system consists of three building blocks: face detection using Histogram of Gradients (HoG) and Support Vector Machine (SVM), facial expression categorization using CNN trained on FER2013 dataset, and finally masking the respective emoji back onto the student's face via homography estimation. (Demo: https://youtu.be/GCjtXw1y8Pw) Our results show that this pipeline is deploy-able in real-time, and is usable in educational settings.
