A Deep Convolutional Neural Network With Fuzzy Rough Sets for FER, in IEEE Access
A Deep Convolutional Neural Network With Fuzzy Rough Sets for FER, in IEEE Access
复制标题
具有模糊粗糙集的 FER 深度卷积神经网络，IEEE Access
作者：
chen xiangjian;Li Di
现有的人脸表情识别方法准确率不高，在实时应用中实用性不足。我们引入2型模糊粗糙集来开发一个2型模糊粗糙卷积神经网络，因为2型模糊粗糙集形成了一个合适的数学工具来表征分类的不确定性。基于第二类模糊粗糙集理论，构造了一个以模糊分类不确定性最小化为目标的神经网络训练优化目标，并给出了第二类模糊粗糙损失的定义和优化方法，使其具有更好的性能.该方法利用第二类模糊粗糙集理论，通过CNN去除原始数据中的噪声样本，降低了粗糙度和不可约性方面的不确定性。最后，将该方法与其他基于算法自适应k-近邻的特征提取和学习技术进行了比较。实验结果表明，2型模糊粗糙集卷积神经网络可以获得更好的性能相比，其他方法。
Existing facial emotion recognition methods do not have high accuracy and are not sufficient practical in real-time applications. We introduce type 2 fuzzy rough sets to develop a Type 2 Fuzzy Rough Convolutional Neural Network, as type 2 fuzzy rough sets form a suitable mathematical tool to characterize uncertainty of classifification. Based on the type 2 fuzzy rough sets theory, we construct an optimization objective for training CNNs by minimizing fuzzy classification uncertainty, and present the defifinition and optimization of type 2 fuzzy rough loss, which can be achieved by better performance. This method could reduce the uncertainty in terms of vagueness and indiscernibility by using type 2 fuzzy rough sets theory and specififically removing noise samples by using CNN from raw data. And finally, compared the proposed method with other feature extraction and learning techniques based on Algorithm Adaption k-Nearest-Neighbors. Experimental results demonstrate that type 2 fuzzy rough sets convolutional neural network could achieve better performances comparing with other methods.