论文标题
面部情绪使用卷积神经网识别
Facial Emotions Recognition using Convolutional Neural Net
论文作者
论文摘要
面部表情因人而异，每个随机图像的亮度，对比度和分辨率都不同。这就是为什么认识面部表情非常困难的原因。本文使用卷积神经网络（CNN）提出了一种有效的系统，以实现七种基本人类情感（愤怒，厌恶，恐惧，幸福，悲伤，惊喜和中立）的识别，该系统可以预测和分配每种情绪的概率。由于深度学习模型从数据中学习，因此，我们提出的系统通过各种预处理步骤处理每个图像以进行更好的预测。每个图像首先通过面部检测算法，以在训练数据集中包含。由于CNN需要大量数据，因此我们使用每个图像上的各种过滤器重复了数据。将大小80*100的预处理图像作为输入传递到CNN的第一层。使用了三个卷积层，然后使用一个合并层和三层密集的层。致密层的辍学率为20％。该模型是通过组合两个公开可用数据集（Jaffe和Kdef）来培训的。 90％的数据用于培训，而10％用于测试。我们使用合并的数据集实现了78.1％的最高精度。此外，我们设计了提出的系统的应用程序，并具有图形用户界面，该界面实时分类情绪。
Facial expressions vary from person to person, and the brightness, contrast, and resolution of every random image are different. This is why recognizing facial expressions is very difficult. This article proposes an efficient system for facial emotion recognition for the seven basic human emotions (angry, disgust, fear, happy, sad, surprise, and neutral), using a convolution neural network (CNN), which predicts and assigns probabilities to each emotion. Since deep learning models learn from data, thus, our proposed system processes each image with various pre-processing steps for better prediction. Every image was first passed through the face detection algorithm to include in the training dataset. As CNN requires a large amount of data, we duplicated our data using various filters on each image. Pre-processed images of size 80*100 are passed as input to the first layer of CNN. Three convolutional layers were used, followed by a pooling layer and three dense layers. The dropout rate for the dense layer was 20%. The model was trained by combining two publicly available datasets, JAFFE and KDEF. 90% of the data was used for training, while 10% was used for testing. We achieved maximum accuracy of 78.1 % using the combined dataset. Moreover, we designed an application of the proposed system with a graphical user interface that classifies emotions in real-time.
