基于蛇形数据访问和管道卷积神经网络的面部表情识别

IF 1.3 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Networks Pub Date : 2022-10-14 DOI:10.1109/IET-ICETA56553.2022.9971645

Chi-Chang Lin, Chia-Yu Hsieh, Ping-Cheng Wu, Ping-Chun Chen, You-Sheng Xiao, Yunqi Fan

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引用次数: 0

摘要

本文提出了一种基于蛇形数据访问和管道卷积神经网络的面部表情识别方法。本文实现了一个由快速卷积运算组成的表情识别系统。我们使用Winograd算法来减少乘法器的数量，并设计数据重用、管道和蛇形数据访问结构来提高芯片的性能。因此，该芯片可以进行高速计算，并实现良好的面部表情识别率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Facial Expression Recognition Based on Snaking Data Access and Pipeline Convolution Neural Network

In this paper, we proposed facial expression recognition based on snaking data access and pipeline convolution neural network. This paper performs an expression recognition system composed of fast convolution operations. We use Winograd algorithm to reduce the number of multipliers and design data reuse, pipeline and Snaking data access structures to increase the performance of the chip. Therefore, the chip can perform high-speed computing and achieve a well facial expression recognition rate.

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来源期刊

IET Networks COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

5.00

自引率

0.00%

发文量

审稿时长

33 weeks

期刊介绍： IET Networks covers the fundamental developments and advancing methodologies to achieve higher performance, optimized and dependable future networks. IET Networks is particularly interested in new ideas and superior solutions to the known and arising technological development bottlenecks at all levels of networking such as topologies, protocols, routing, relaying and resource-allocation for more efficient and more reliable provision of network services. Topics include, but are not limited to: Network Architecture, Design and Planning, Network Protocol, Software, Analysis, Simulation and Experiment, Network Technologies, Applications and Services, Network Security, Operation and Management.