Real-Time Facial Affective Computing on Mobile Devices
Real-Time Facial Affective Computing on Mobile Devices
复制标题
DOI：
10.3390/s20030870
复制
发表时间：
2020-02-01
期刊：
影响因子：
3.9
通讯作者：
Chen, Rung-Ching
中科院分区：
文献类型：
--
作者：
Guo, Yuanyuan;Xia, Yifan;Chen, Rung-Ching
卷积神经网络(CNN)已经成为各种计算机视觉和模式识别任务的最先进的方法之一，其中包括面部情感计算。虽然CNN在人脸情感计算中取得了令人印象深刻的结果，但CNN的计算复杂度也显著增加。这意味着高性能硬件通常是不可或缺的。因此，大多数现有的CNN对于存储、存储和计算能力有限的移动设备来说不够通用。本文主要研究了面向实时人脸情感计算任务的移动设备CNN的设计与实现。我们提出了一种轻量级的CNN结构，很好地平衡了性能和计算复杂度。实验结果表明，与现有方法相比，该结构在保持较低计算复杂度的同时，获得了较高的性能。通过在实际移动设备上实现的实时人脸情感计算应用，从移动设备的速度、内存和存储消耗方面论证了CNN架构的可行性。
Convolutional Neural Networks (CNNs) have become one of the state-of-the-art methods for various computer vision and pattern recognition tasks including facial affective computing. Although impressive results have been obtained in facial affective computing using CNNs, the computational complexity of CNNs has also increased significantly. This means high performance hardware is typically indispensable. Most existing CNNs are thus not generalizable enough for mobile devices, where the storage, memory and computational power are limited. In this paper, we focus on the design and implementation of CNNs on mobile devices for real-time facial affective computing tasks. We propose a light-weight CNN architecture which well balances the performance and computational complexity. The experimental results show that the proposed architecture achieves high performance while retaining the low computational complexity compared with state-of-the-art methods. We demonstrate the feasibility of a CNN architecture in terms of speed, memory and storage consumption for mobile devices by implementing a real-time facial affective computing application on an actual mobile device.