Classification of facial data with capsule networks

The process of classifying face data is one of the deep learning topics that has been studied extensively. In this regard, convolutional neural networks are among the most preferred deep learning applications because they are easy to implement and give successful results. On the other hand, the pooling layer in convolutional neural networks causes information loss in the data. In addition, convolutional neural networks perform the training process by ignoring the relative states of the components in the data. As a solution to this situation, capsule networks are one of the recommended deep learning methods. In this thesis study, three different studies were conducted and the results were examined in terms of performance and efficiency.