Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis

Cross-domain synthesizing realistic faces to learn deep models has attracted increasing attention for facial expression analysis as it helps to improve the performance of expression recognition accuracy despite having small number of real training images. However, learning from synthetic face images can be problematic due to the distribution discrepancy between low-quality synthetic images and real face images and may not achieve the desired performance when the learned model applies to real world scenarios. To this end, we propose a new attribute guided face image synthesis to perform a translation between multiple image domains using a single model. In addition, we adopt the proposed model to learn from synthetic faces by matching the feature distributions between different domains while preserving each domain's characteristics. We evaluate the effectiveness of the proposed approach on several face datasets on generating realistic face images. We demonstrate that the expression recognition performance can be enhanced by benefiting from our face synthesis model. Moreover, we also conduct experiments on a near-infrared dataset containing facial expression videos of drivers to assess the performance using in-the-wild data for driver emotion recognition.Comment: 8 pages, 8 figures, 5 tables, accepted by FG 2019. arXiv admin note: substantial text overlap with arXiv:1905.0028

Investigating the Impact of New Technologies and E-learning on Learners' Emotions and Moods

Due to the lack of direct communication between teacher and learner in the e-learning environment, learners in this environment need education with good support and personal redemption. Using this research, you can have new technology in e-learning on the emotions and moods of learners. The statistical population of Farzanegan 7 high school math students is 75 people. In order to find 5 different types of learners' emotions, students are divided into 5 groups of 15, each of which is specifically exposed to different conditions. You have to experience happiness, anger, fear, frustration and hatred, and their face information is posted through the webcam. Your videos are recorded and the learners' emotions are measured and detected in different situations according to the neural network's deep learning algorithms by the Face Reader incremental software system. There has been a research method of designing a fuzzy expert system and a fuzzy inference system. And makes learners discover. And reject. Created within ranges. This change indicates that it increases the feeling and increases the negative feeling. Keywords: Internet of Things, e-learning, learners' emotions