Qingdao Huanghai College, Qingdao, Shandong 266427, China.
Comput Intell Neurosci. 2022 Aug 5;2022:4288187. doi: 10.1155/2022/4288187. eCollection 2022.
In recent years, in the field of virtual reality, in more and more scenes, users interact with hardware or programs through facial expressions. In order to give full play to the advantages of program interaction between virtual reality devices and users, this paper proposes a mobile virtual reality expression recognition system combined with convolution neural network. Based on the optimized AlexNet network, an expression recognition algorithm is constructed and combined with LBP feature mapping technology to improve the performance of the algorithm. At the same time, according to the nature and characteristics of mobile virtual reality devices, the user face information acquisition algorithm is optimized. The performance test results of the expression recognition system show that the recognition accuracy of the system is higher than that of the traditional convolution neural network expression recognition algorithm, and the maximum difference is greater than 10%. At the same time, the average running speed of the whole system is about 37 ms, which can meet the accuracy and real-time requirements of expression recognition in virtual reality interaction. The experimental results show that the expression recognition system proposed in this paper can be applied to mobile virtual reality devices. At the same time, it also provides new ideas for industry researchers to optimize the identification function.
近年来，在虚拟现实领域，在越来越多的场景中，用户通过面部表情与硬件或程序进行交互。为了充分发挥虚拟现实设备与用户之间程序交互的优势，本文提出了一种结合卷积神经网络的移动虚拟现实表情识别系统。在优化的 AlexNet 网络的基础上，构建了表情识别算法，并结合 LBP 特征映射技术，提高了算法的性能。同时，根据移动虚拟现实设备的性质和特点，优化了用户面部信息采集算法。表情识别系统的性能测试结果表明，该系统的识别精度高于传统的卷积神经网络表情识别算法，最大差异大于 10%。同时，整个系统的平均运行速度约为 37ms，能够满足虚拟现实交互中表情识别的准确性和实时性要求。实验结果表明，本文提出的表情识别系统可以应用于移动虚拟现实设备，同时也为行业研究人员优化识别功能提供了新的思路。
