论文标题
Emotionnet Nano：实时面部表达识别的有效的深卷卷卷神经网络设计
EmotionNet Nano: An Efficient Deep Convolutional Neural Network Design for Real-time Facial Expression Recognition
论文作者
论文摘要
尽管深度学习的最新进展已导致面部表达分类（FEC）的显着改善，但仍然是这种系统广泛部署的瓶颈的主要挑战是它们的建筑和计算复杂性很高。考虑到各种FEC应用程序的运营要求，例如安全，营销，学习和辅助生活，这尤其具有挑战性，在这种要求上，需要对低成本嵌入式设备进行实时要求。由于需要在低成本嵌入式设备上实时执行FEC的需求，这项研究提出了Emotionnet Nano，这是一种通过人机协作设计策略创建的有效的深度卷积神经网络，该策略与人类的经验相结合，以与机器的精力和速度相结合，以实时的设计将人类的经验结合起来。呈现了两个不同的EmotionNet Nano变体，每种变体的架构复杂性和准确性之间都有不同的权衡。使用CK+面部表达基准数据集的实验结果表明，所提出的EmotionNet Nano网络表明，与FEC网络中最新的ART相媲美的精度，同时需要较少的参数（例如，在较高准确度上，23 $ \乘以$ \乘以少于23 $ \乘以）。 Furthermore, we demonstrate that the proposed EmotionNet Nano networks achieved real-time inference speeds (e.g. $>25$ FPS and $>70$ FPS at 15W and 30W, respectively) and high energy efficiency (e.g. $>1.7$ images/sec/watt at 15W) on an ARM embedded processor, thus further illustrating the efficacy of EmotionNet Nano for deployment on embedded设备。
While recent advances in deep learning have led to significant improvements in facial expression classification (FEC), a major challenge that remains a bottleneck for the widespread deployment of such systems is their high architectural and computational complexities. This is especially challenging given the operational requirements of various FEC applications, such as safety, marketing, learning, and assistive living, where real-time requirements on low-cost embedded devices is desired. Motivated by this need for a compact, low latency, yet accurate system capable of performing FEC in real-time on low-cost embedded devices, this study proposes EmotionNet Nano, an efficient deep convolutional neural network created through a human-machine collaborative design strategy, where human experience is combined with machine meticulousness and speed in order to craft a deep neural network design catered towards real-time embedded usage. Two different variants of EmotionNet Nano are presented, each with a different trade-off between architectural and computational complexity and accuracy. Experimental results using the CK+ facial expression benchmark dataset demonstrate that the proposed EmotionNet Nano networks demonstrated accuracies comparable to state-of-the-art in FEC networks, while requiring significantly fewer parameters (e.g., 23$\times$ fewer at a higher accuracy). Furthermore, we demonstrate that the proposed EmotionNet Nano networks achieved real-time inference speeds (e.g. $>25$ FPS and $>70$ FPS at 15W and 30W, respectively) and high energy efficiency (e.g. $>1.7$ images/sec/watt at 15W) on an ARM embedded processor, thus further illustrating the efficacy of EmotionNet Nano for deployment on embedded devices.
