Research and Implementation of Facial Expression Recognition Algorithm Based on Machine Learning

Traditional information security management methods can provide a degree of personal information protection but remain vulnerable to issues such as data breaches and password theft. To bolster information security, facial expression recognition offers a promising alternative. To achieve efficient and accurate facial expression recognition, we propose a lightweight neural network algorithm called T-SNet (Teacher-Student Net). In our approach, the teacher model is an enhanced version of ResNet18, incorporating fine-grained feature extraction modules and pre-trained on the MS-Celeb-1M facial dataset. The student model uses the lightweight convolutional neural network ShuffleNetV2, with the model's accuracy further improved by optimizing the distillation loss function. This design carefully considers the key features of facial expressions, determines the most effective extraction techniques, and classifies and recognizes these features. To evaluate the performance of our algorithm, we conducted comparative experiments against state-of-the-art facial expression recognition methods. The results show that our approach outperforms existing methods in both recognition accuracy and efficiency.

@article{Xie2024,
  title     = {Research and Implementation of Facial Expression Recognition Algorithm Based on Machine Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Xinjiu Xie and Jinxue Huang},
  doi       = {10.14569/IJACSA.2024.0150980},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150980}
}