Face Recognition Technology Improving Using Convolutional Neural Networks
Oleksandr Miakshyn, Pavlo Anufriiev, Yevgen Bashkov · 2021
The principles of the face recognition technology building, which are based on the architecture of convolutional neural networks, are considered. Attention is paid to such stages of recognition technology as detection, feature extraction, identification and verification. Also, neural network learning methods, cost functions are reviewed. FaceNet and OpenFace architectures were chosen for further analysis. The OpenFace architecture has been modified through the classifier learning method. TensorFlow and Keras libraries, LFW and Pin Faces network learning datasets were used to build the neural network structure. The ORL Faces dataset was used for testing. The accuracy of training reached 98%. The improvement after modification on the test dataset reached 17%. It was decided to use a modified architecture for implementation in the face recognition system under development.