Network-Simulated Generation of Human Faces with Expressions and Orientations for Deep Learning Classification
Kornprom Pikulkaew1, Ekkarat Boonchieng2, Waraporn Boonchieng3, Varin Chouvatut4
1Kornprom Pikulkaew, Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
2Ekkarat Boonchieng, Center of Excellence in Community Health Informatics, Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
3Waraporn Boonchieng, Faculty of Public Health, Chiang Mai University, Chiang Mai, Thailand
4Varin Chouvatut*, Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand.
Manuscript received on November 15, 2019. | Revised Manuscript received on 20 November, 2019. | Manuscript published on December 10, 2019. | PP: 2178-2185 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7491129219/2019©BEIESP | DOI: 10.35940/ijitee.B7491.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Classification, Deep Learning, Facial Expression, Generative Adversarial Networks, Human Faces, Orientation.
Scope of the Article: Classification