Brain-Inspired Deep Networks for Facial Expression Recognition

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Zeinali Nafiseh; FAEZ KARIM; SEIFZADEH SAHAR

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Journal Paper

Paper Information

Journal: Frontiers in Biomedical Technologies Year:2020 | Volume:7 | Issue:3 Page(s): 169-176

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Information Journal Paper

Title

Brain-Inspired Deep Networks for Facial Expression Recognition

Author(s)

Zeinali Nafiseh | FAEZ KARIM | SEIFZADEH SAHAR | Issue Writer Certificate

Keywords

Deep Learning

Small Database

Face Expression

Res-Net Network

Self-Made Dataset

Abstract

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.

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References

Facial Expression Recognition with Convolution Neural Networks: Coping with Few Data and the Training Sample Order

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APA: Copy

Zeinali, Nafiseh, FAEZ, KARIM, & SEIFZADEH, SAHAR. (2020). Brain-Inspired Deep Networks for Facial Expression Recognition. FRONTIERS IN BIOMEDICAL TECHNOLOGIES, 7(3), 169-176. SID. https://sid.ir/paper/671607/en

Vancouver: Copy

Zeinali Nafiseh, FAEZ KARIM, SEIFZADEH SAHAR. Brain-Inspired Deep Networks for Facial Expression Recognition. FRONTIERS IN BIOMEDICAL TECHNOLOGIES[Internet]. 2020;7(3):169-176. Available from: https://sid.ir/paper/671607/en

IEEE: Copy

Nafiseh Zeinali, KARIM FAEZ, and SAHAR SEIFZADEH, “Brain-Inspired Deep Networks for Facial Expression Recognition,” FRONTIERS IN BIOMEDICAL TECHNOLOGIES, vol. 7, no. 3, pp. 169–176, 2020, [Online]. Available: https://sid.ir/paper/671607/en

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