Recognition of Affect in the Wild Using Deep Neural Networks

D. Kollias, M. Nicolaou, I. Kotsia, Guoying Zhao, S. Zafeiriou
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引用次数: 127

Abstract

In this paper we utilize the first large-scale "in-the-wild" (Aff-Wild) database, which is annotated in terms of the valence-arousal dimensions, to train and test an end-to-end deep neural architecture for the estimation of continuous emotion dimensions based on visual cues. The proposed architecture is based on jointly training convolutional (CNN) and recurrent neural network (RNN) layers, thus exploiting both the invariant properties of convolutional features, while also modelling temporal dynamics that arise in human behaviour via the recurrent layers. Various pre-trained networks are used as starting structures which are subsequently appropriately fine-tuned to the Aff-Wild database. Obtained results show premise for the utilization of deep architectures for the visual analysis of human behaviour in terms of continuous emotion dimensions and analysis of different types of affect.
利用深度神经网络识别野外情感
在本文中,我们利用第一个大规模的“野外”(Aff-Wild)数据库,该数据库根据价唤醒维度进行注释,以训练和测试端到端深度神经架构,用于基于视觉线索估计连续情感维度。所提出的架构是基于联合训练卷积(CNN)和循环神经网络(RNN)层,从而利用卷积特征的不变特性,同时也通过循环层建模人类行为中出现的时间动态。各种预训练的网络被用作初始结构,随后适当地微调到af - wild数据库。所得结果为利用深度架构从连续情感维度和不同类型情感分析的角度对人类行为进行视觉分析提供了前提。
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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