A Deep Convolutional Neural Network With Fuzzy Rough Sets for FER

Abstract

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.

Keywords

• Convolutional neural network
• Type 2 fuzzy rough sets
• algorithm adaption k-nearest-neighbors