Abstract: Facial expression recognition and detection are prominent research areas in computer vision and deep learning,widely applied across various scenarios.However,traditional expression detection methods perform poorly under unconstrained conditions,while deep learning approaches face challenges such as low feature discriminability and susceptibility to posture and expression variations.To address these issues,this paper proposes an IML-SSD algorithm based on SSD network model reconstruction and center loss optimization to enhance the accuracy and robustness of facial expression detection.Firstly,this paper introduces a fast SSD-based facial expression detection algorithm optimized through network reconstruction.By restructuring the base and auxiliary layers of the SSD model,the algorithm achieves improved recognition speed,accuracy,and robustness.Subsequently,the SSD algorithm is further optimized by incorporating a center loss function,which enhances the aggregation of features within the same category while increasing separation between different categories,thereby strengthening the discriminative capability of facial expression features.Test results demonstrate that the proposed algorithm outperforms comparative methods,achieving approximately a 6.5 percentage points increase in mAP values on the FERPlus dataset.

Key words: Facial expression detection, SSD Refactoring, Deep neural network