Deep Convolutional Neural Networks for facial expression recognition

A. Uçar
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引用次数: 18

Abstract

Facial expression recognition is a very active research topic due to its potential applications in the many fields such as human-robot interaction, human-machine interfaces, driving safety, and health-care. Despite of the significant improvements, facial expression recognition is still a challenging problem that wait for more and more accurate algorithms. This article presents a new model that is capable of recognizing facial expression by using deep Convolutional Neural Network (CNN). The CNN model is generated by using Caffe in Digits environment. Moreover, it is trained and tested on NVIDIA Tegra TX1 embedded development platform including a 250 Graphics Processing Unit (GPU) CUDA cores and Quadcore ARM Cortex A57 processor. The proposed model is applied to address the facial expression problem on the publicly available two expression databases, the JAFFE database and the Cohn-Kanade database.
用于面部表情识别的深度卷积神经网络
面部表情识别在人机交互、人机界面、驾驶安全、医疗保健等领域具有潜在的应用前景,是一个非常活跃的研究课题。尽管有了显著的进步,面部表情识别仍然是一个具有挑战性的问题,需要越来越精确的算法。本文提出了一种基于深度卷积神经网络(CNN)的面部表情识别新模型。CNN模型是在Digits环境中使用Caffe生成的。此外,它在NVIDIA Tegra TX1嵌入式开发平台上进行了训练和测试,该平台包含250个图形处理单元(GPU) CUDA内核和四核ARM Cortex A57处理器。该模型在JAFFE和Cohn-Kanade两个公开的表情数据库上进行了面部表情问题的求解。
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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