Application of Facial Expression Visual Recognition for Humanoid Robots Based on SAV Network Images

As the core carrier for achieving human-machine collaborative work in intelligent manufacturing scenarios, one of the key indicators of the bionic intelligence level of humanoid robots is the real-time and accurate perception of human emotional states driven by visual perception. Facial expression recognition (FER) is the core supporting element of this perceptual ability. The existing FER algorithm generally suffers from insufficient capture of facial region correlation, which makes it difficult to meet the practical needs of natural collaboration between humanoid robots and human operators. This is also one of the core scientific issues in the development of industrial humanoid robots that combine emotions and intelligence. This article addresses this issue by combining self attention mechanism with VGG16-BN network, proposing a novel visual perception network called SAVNet, and integrating it into the expression recognition system of humanoid robots. The proposed network embedding visual self attention mechanism (VSAM) enables the model to accurately capture the associated features of key facial regions within the sensory field, effectively improving the FER performance of industrial humanoid robots in complex industrial environments. The evaluation results on the FER2013, RAF-DB, CK+, and AffectNet standard datasets show that the VSAM module has significant effects in optimizing the internal representation of features and enhancing the model's robustness against industrial scene interference. For example, after 50 rounds of training on the RAF-DB dataset, the highest classification accuracy of VGG16 on the RAF-DB test set was 80.07%, while the proposed model achieved an expression recognition rate of 82.26%, which was 2.19% higher than that of VGG16. The practical verification of applying SAVNet to the prototype system of humanoid robots shows that this method not only achieves leading recognition accuracy on standard datasets, but also can stably respond to human expression changes in real industrial operation scenarios. This provides reliable visual perception technology support for the safe collaboration, emotional interaction, and intelligent services between industrial humanoid robots and operators, promoting the engineering implementation process of bionic intelligence in industrial humanoid robots.