The Regularization Effect of Pre-activation Batch Normalization on Convolutional Neural Network Performance for Face Recognition System Paper

Face recognition is of pronounced significance to real-world applications such as video surveillance systems, human computing interaction, and security systems. This biometric authenticating system encompasses rich real human face characteristics. As such, it has been one of the important research topics in computer vision. Face recognition systems based on deep learning approaches suffer from internal covariate shift problems that cause gradients to explode or gradient disappearance, which leads to improper network training. Improper network training causes network overfitting and computational load. This reduces recognition accuracy and slows down network speed. This paper proposes a modified pre-activation batch normalization convolutional neural network by adding a batch normalization layer after each convolutional layer within each of the four convolutional units of the proposed model. The performance of the proposed model is validated with a new dataset, AS-Darmaset, which is built out of two publicly available databases. This paper compared the convergence behavior of four different CNN models: the Pre-activation Batch Normalization CNN model, the Traditional CNN without Batch Normalization, the Post-Activation Batch Normalization CNN model, and the Sparse Batch Normalization CNN Architecture. The evaluation results show that the recognition performance of Pre-activation BN CNN has training and validation accuracies of 100.00% and 99.87%, the Post activation Batch normalization has 100.00% and 99.81%, and the traditional CNN without BN has 96.50% and 98.93%. The sparse batch normalization CNN has 96.25% and 97.60% success rate, respectively. The result shows that the Pre-activation BN CNN model is more effective than the other three deep learning models.

@article{Darma2021,
  title     = {The Regularization Effect of Pre-activation Batch Normalization on Convolutional Neural Network Performance for Face Recognition System Paper},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {11},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Abu Sanusi Darma and Fatma Susilawati Binti Mohamad},
  doi       = {10.14569/IJACSA.2021.0121135},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121135}
}