Facial Expression Recognition Using Data Augmentation and Transfer Learning.

The world of technology has dramatically benefited from artificial intelligence's rapid development. Deep learning and machine learning algorithms have attained remarkable success in various applications, including recommendation system, pattern recognition, classification system, etc., where traditional algorithms have failed to fulfill the requirements of humans in real time. A person's thoughts, conduct, and feelings are greatly influenced by their emotions. In this work proposed method for FER is based on existing VGG16 and concatenates additional layers. The model consists of the preprocessing stage based on the median filter and data augmentation, and classification based on pertained VGG16 with transfer learning added four layers to the existing vgg16 that already trained on the ImageNet dataset. The model proposed attained an accuracy of 90% on FER2013 test set. It is observed that the proposed method outperformed the previous works. [ABSTRACT FROM AUTHOR]