Архитектура обобщенных сверточных нейронных сетей

The structure of the generalized convolutional neural networks which allow advantages of classical convolutional neural networks to be used with capabilities of new network class for problem of human face recognition was described in this article.В статье рассмотрена архитектура обобщенных сверточных нейронных сетей, позволяющих использовать преимущества классических сверточных нейронных сетей с дополнительными возможностями нового класса в задачах распознавания человека по фотопортрету

When Face Recognition Meets with Deep Learning: an Evaluation of Convolutional Neural Networks for Face Recognition

Deep learning, in particular Convolutional Neural Network (CNN), has achieved promising results in face recognition recently. However, it remains an open question: why CNNs work well and how to design a 'good' architecture. The existing works tend to focus on reporting CNN architectures that work well for face recognition rather than investigate the reason. In this work, we conduct an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a common ground to make our work easily reproducible. Specifically, we use public database LFW (Labeled Faces in the Wild) to train CNNs, unlike most existing CNNs trained on private databases. We propose three CNN architectures which are the first reported architectures trained using LFW data. This paper quantitatively compares the architectures of CNNs and evaluate the effect of different implementation choices. We identify several useful properties of CNN-FRS. For instance, the dimensionality of the learned features can be significantly reduced without adverse effect on face recognition accuracy. In addition, traditional metric learning method exploiting CNN-learned features is evaluated. Experiments show two crucial factors to good CNN-FRS performance are the fusion of multiple CNNs and metric learning. To make our work reproducible, source code and models will be made publicly available.Comment: 7 pages, 4 figures, 7 table

Вплив параметрів згорточних нейронних мереж на якість розпізнавання людини за фотопортретом

The purpose of the study - to develop guidelines for choosing the parameters of convolutional neural networks for solving the problem of human face recognition. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/2851