DeXpression: Deep Convolutional Neural Network for Expression Recognition
DeXpression: Deep Convolutional Neural Network for Expression Recognition
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发表时间：
2015-09
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通讯作者：
Peter Burkert;Felix Trier;Muhammad Zeshan Afzal;A. Dengel;M. Liwicki
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作者：
Peter Burkert;Felix Trier;Muhammad Zeshan Afzal;A. Dengel;M. Liwicki
我们提出了一种用于面部表情识别的卷积神经网络（CNN）架构。所提出的架构独立于任何手工特征提取，并且比早期提出的基于卷积神经网络的方法性能更好。我们将网络学习的自动提取的特征可视化，以便更好地理解。标准数据集，即扩展的科恩-卡纳德（CKP）和MMI面部表情数据库用于定量评价。在CKP集上，使用CNN的当前最先进的方法实现了99.2%的准确率。对于MMI数据集，目前情感识别的最佳准确率为93.33%。所提出的架构实现了CKP的99.6%和MMI的98.63%，因此比使用CNN的最新技术表现更好。自动面部表情识别在人机交互和安全系统等方面有着广泛的应用。这是因为非语言线索是重要的交际形式，在人际交往中起着举足轻重的作用。所提出的架构的性能认可的功效和可靠的使用所提出的工作为真实的世界的应用程序。
We propose a convolutional neural network (CNN) architecture for facial expression recognition. The proposed architecture is independent of any hand-crafted feature extraction and performs better than the earlier proposed convolutional neural network based approaches. We visualize the automatically extracted features which have been learned by the network in order to provide a better understanding. The standard datasets, i.e. Extended Cohn-Kanade (CKP) and MMI Facial Expression Databse are used for the quantitative evaluation. On the CKP set the current state of the art approach, using CNNs, achieves an accuracy of 99.2%. For the MMI dataset, currently the best accuracy for emotion recognition is 93.33%. The proposed architecture achieves 99.6% for CKP and 98.63% for MMI, therefore performing better than the state of the art using CNNs. Automatic facial expression recognition has a broad spectrum of applications such as human-computer interaction and safety systems. This is due to the fact that non-verbal cues are important forms of communication and play a pivotal role in interpersonal communication. The performance of the proposed architecture endorses the efficacy and reliable usage of the proposed work for real world applications.