Comparison of Deep Learning Algorithms for Facial Keypoints Detection

A study on facial keypoints recognition using five deep learning models is presented in this paper, focusing on emphasizing the significance of striking the right balance between model complexity and training data volume. Three convolutional neural networks - LeNet-5, AlexNet, and VGG-16 - are also included in the models that are being presented, along with two custom architectures. The first architecture has a very simple design, while the second one has a regular depth. The study utilizes a small publicly available dataset and focuses on the relationship between model complexity and accuracy in facial keypoints detection. While the more complex custom Neural Network model achieved high accuracy in facial keypoints identification, the simpler custom Neural Network model could not achieve high performance.