Open AccessNetwork-Simulated Generation of Human Faces with Expressions and Orientations for Deep Learning Classification
Author(s) -
Kornprom Pikulkaew,
Ekkarat Boonchieng,
Waraporn Boonchieng,
Varin Chouvatut
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b7491.129219
Subject(s) - deep learning , artificial intelligence , computer science , face (sociological concept) , field (mathematics) , task (project management) , key (lock) , machine learning , pattern recognition (psychology) , process (computing) , orientation (vector space) , generative grammar , deep belief network , image (mathematics) , facial expression , mathematics , engineering , social science , geometry , computer security , systems engineering , sociology , pure mathematics , operating system
Human face recognition is a complex task, and it is important as it can be applied to assist people worldwide, such as those in the medical field or security. For example, human faces can be used for detecting pain or emotion. Nevertheless, a drawback of deep learning methods that need a lot of data to process is key. In this study, a deep-learning-based technique, which is used to classify, that generates a synthetic image of the facial expression and orientation by utilizing the Wasserstein generative adversarial network (WGAN) is presented. The WGAN can improve the performance of the deep learning method. The proposed system certainly generates images with a small number of datasets compared to the large datasets. This research aims to solve the problem of deep learning by increasing the accuracy of the system. The generated output coincides with the real image dataset. The application using ResNet-50 and RetinaNet as a pre-model for the prediction and detection of the human faces revealed a rapid prediction time and accuracy during the assessment test.