Generative Adversarial Networks for Improving Face Classification
Abstract
Facial recognition can be applied in a wide variety of cases, including entertainment
purposes and biometric security. In this thesis we take a look at improving
the results of an existing facial recognition approach by utilizing generative adversarial
networks to improve the existing dataset.
The training data was taken from the LFW dataset[4] and was preprocessed
using OpenCV[2] for face detection. The faces in the dataset was cropped and
resized so every image is the same size and can easily be passed to a convolutional
neural network. To the best of our knowledge no generative adversarial network
approach has been applied to facial recognition by generating training data for
classification with convolutional neural networks.
The proposed approach to improving face classification accuracy is not improving
the classification algorithm itself but rather improving the dataset by generating
more data. In this thesis we attempt to use generative adversarial networks
to generate new data. We achieve an impressive accuracy of 99.42% with 3
classes, which is an improvement of 1.74% compared to not generating any new
data.
Description
Master's thesis Information- and communication technology IKT590 - University of Agder 2017