Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 7 3 2020 09 30 170 177 Nafiseh Zeinali Department of Electrical Computer and Biomedical Engineering, Qazvin Branch, Islamic University, Qazvin, Iran. Karim Faez Electrical Engineering Department, Amirkabir University of Technology Tehran, Iran Sahar Seifzadeh Division of Cognitive Neuroscience, University of Tabriz, Tabriz, Iran AND Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran 2020 08 15 2020 09 21 Purpose: One of the essential problems in deep-learning face recognition research is the use of self-made and less counted data sets, which forces the researcher to work on duplicate and provided data sets. In this research, we try to resolve this problem and get to high accuracy. Materials and Methods: In the current study, the goal is to identify individual facial expressions in the image or sequence of images that include identifying ten facial expressions. Considering the increasing use of deep learning in recent years, in this study, using the convolution networks and, most importantly, using the concept of transfer learning, led us to use pre-trained networks to train our networks. Results: One way to improve accuracy in working with less counted data and deep-learning is to use pre-trained using pre-trained networks. Due to the small number of data sets, we used the techniques for data augmentation and eventually tripled the data size. These techniques include: rotating 10 degrees to the left and right and eventually turning to elastic transmation. We also applied deep Res-Net's network to public data sets existing for face expression by data augmentation. Conclusion: We saw a seven percent increase in accuracy compared to the highest accuracy in previous work on the considering dataset. https://fbt.tums.ac.ir/index.php/fbt/article/view/268 https://fbt.tums.ac.ir/index.php/fbt/article/download/268/168