论文标题
开发面部压力分析和表达识别平台
On Developing Facial Stress Analysis and Expression Recognition Platform
论文作者
论文摘要
这项工作代表了沉浸式数字学习平台的系统面部表达识别和面部压力分析算法的实验和开发过程。该系统从用户网络摄像头检索,并使用人工神经网络(ANN)算法对其进行评估。 ANN输出信号可用于评分和改进学习过程。将ANN适应新系统可能需要大量的实施工作,也需要重复ANN培训。还存在与运行ANN所需的最小硬件有关的限制。为了超过这些约束,提出了一些可能的面部表达识别和面部压力分析算法的可能实现。新解决方案的实施使得提高识别面部表情的准确性并提高其响应速度成为可能。实验结果表明,与社交设备相比,使用开发的算法可以以更高的速度检测心率。
This work represents the experimental and development process of system facial expression recognition and facial stress analysis algorithms for an immersive digital learning platform. The system retrieves from users web camera and evaluates it using artificial neural network (ANN) algorithms. The ANN output signals can be used to score and improve the learning process. Adapting an ANN to a new system can require a significant implementation effort or the need to repeat the ANN training. There are also limitations related to the minimum hardware required to run an ANN. To overpass these constraints, some possible implementations of facial expression recognition and facial stress analysis algorithms in real-time systems are presented. The implementation of the new solution has made it possible to improve the accuracy in the recognition of facial expressions and also to increase their response speed. Experimental results showed that using the developed algorithms allow to detect the heart rate with better rate in comparison with social equipment.