THESIS

Face Synthesis Using Modified Hyperstyle Architecture

ABSTRACT

Face recognition is the most stable and robust biometric technique for identifying and authenticating human faces. However, training face recognition models using a deep learing architecture requires large training images. In addition, labeling face image collections manually is a time-consuming and costly process. Augmenting face images using the HyperStyle architecture can solve these issues. Experimental results obtained using the ResNet-50 architecture trained on a dataset of 44,000 face images (the original FaceScrub dataset combined with the HyperStyle Age and HyperStyle Smile augmented images) demonstrate that the model achieved an F1-score of 79%, therefore outperforming the model trained using the original FaceScrub dataset without HyperStyle augmentation, i.e., F1-score of 63%. ResNet-50 with an FI-score of 82% outperformed other CNN models i.e., VGGNet-1 6 (60%), MobileNet V3 Small (65%). SEResNet18 (81%). HyperStyle modifications on the pre-processing and post- processing achieved promising results on model performance. ResNet-50 model trained using combination of the original FaceScrub dataset and the modified Hyper Style synthesized dataset obtained an F1-Score of 83%.

PUBLICATION

• HyperStyle-Based Data Augmentation to Improve the Performance of Face Recognition Model (link)
• Analysis of Face Data Augmentation in Various Poses for Face Recognition Model