A Comparative Study on Convolutional Neural Network Based Face Recognition

A Comparative Study on Convolutional Neural Network Based Face Recognition

연구 분야: Artificial Intelligence

학회: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

This paper presents a comparative study to recognize faces from a customized dataset of 10 identities of different celebrities using Convolutional Neural Network based models such as AlexNet, VGG16, VGG19 and MobileNet. These pre-trained models previously trained on ImageNet dataset are used with the application of Transfer Learning and Fine Tuning. For our experiment we used Keras API with TensorFlow backend written in Python. The performance analysis includes training, validation, and testing on different images created from original dataset. The validation accuracy of VGG19 model is found better than the other three but MobileNet model showed better test accuracy.