A Fuzzy Neural Network Enabled Deep Subspace Domain Adaptive Fusion Approaches for Facial Expression Recognition

There are many interference factors in the real world that are not related to facial expressions, such as background, lighting intensity, and changes in image resolution. In order to address the problem of facial expression recognition, this article proposes a fuzzy neural network enabled deep subspace domain adaptive fusion (FNN-DSDAF) by integrating the fuzzy logic module into the convolutional network structure. The fuzzy logic module integrates the powerful learning ability of deep networks can learn a set of robust Gaussian membership functions through a series of learnable Gaussian membership functions and logical operations. When training the classical normalized exponential loss function to force different categories of samples to maintain a certain distance in the learned feature space, FNN-DSDAF further uses the local hold loss function to make the local clusters within each category of samples more compact. The experimental results demonstrated that the fuzzy neural network had good feature decoupling ability and was suitable for facial expression understanding in real-world scenarios, and improve the accuracy, reliability, and robustness.