Expression Recognition Method Based on Convolutional Neural Network and Capsule Neural Network

Convolutional neural networks struggle to accurately handle changes in angles and twists in the direction of images, which affects their ability to recognize patterns based on internal feature levels. In contrast, CapsNet overcomes these limitations by vectorizing information through increased directionality and magnitude, ensuring that spatial information is not overlooked. Therefore, this study proposes a novel expression recognition technique called CAPSULE-VGG, which combines the strengths of CapsNet and convolutional neural networks. By refining and integrating features extracted by a convolutional neural network before introducing them into CapsNet, our model enhances facial recognition capabilities. Compared to traditional neural network models, our approach offers faster training pace, improved convergence speed, and higher accuracy rates approaching stability. Experimental results demonstrate that our method achieves recognition rates of 74.14% for the FER2013 expression dataset and 99.85% for the CK+ expression dataset. By contrasting these findings with those obtained using conventional expression recognition techniques and incorporating CapsNet’s advantages, we effectively address issues associated with convolutional neural networks while increasing expression identification accuracy.