Design of Real-Time Face Recognition and Emotion Recognition on Humanoid Robot Using Deep Learning

  • Muhammad Iqbal Department of Electrical Engineering, Faculty of Engineering, Universitas Sriwijaya
  • Bhakti Yudho Suprapto Department of Electrical Engineering, Faculty of Engineering, Universitas Sriwijaya
  • Hera Hikmarika Department of Electrical Engineering, Faculty of Engineering, Universitas Sriwijaya
  • Hermawati Hermawati Department of Electrical Engineering, Faculty of Engineering, Universitas Sriwijaya
  • Suci Dwijayanti Department of Electrical Engineering, Faculty of Engineering, Universitas Sriwijaya

Abstract

A robot is capable of mimicking human beings, including recognizing their faces and emotions. However, current studies of the humanoid robot have not been implemented in the real-time system. In addition, face recognition and emotion recognition have been treated as separate problems. Thus, for real-time application on a humanoid robot, this study proposed a combination of face recognition and emotion recognition. Face and emotion recognition systems were developed concurrently in this study using convolutional neural network architectures. The proposed architecture was compared to the well-known architecture, AlexNet, to determine which architecture would be better suited for implementation on a humanoid robot. Primary data from 30 respondents was used for face recognition. Meanwhile, emotional data were collected from the same respondents and combined with secondary data from a 2500-person dataset. Surprise, anger, neutral, smile, and sadness were among the emotions. The experiment was carried out in real-time on a humanoid robot using the two architectures. Using the AlexNet model, the accuracy of face and emotion recognition was 87 % and 70 %, respectively. Meanwhile, the proposed architecture achieved accuracy rates of 95 % for face recognition and 75 % for emotion recognition, respectively. Thus, the proposed method performs better in terms of recognizing faces and emotions, and it can be implemented on a humanoid robot.

Keywords: Accuracy, Convolution Neural Network, Emotion Recognition, Face Recognition, Humanoid Robot

Downloads

Download data is not yet available.

References

A. Dzedzickis, A. Kaklauskas, and V. Bucinskas, “Human emotion recognition: Review of sensors and methods,” Sensors, vol. 20, no. 3, p.592., 2020.

S. Madanny, Samsuryadi,N. Yusliani, “Face Recognition Using Hyper Sausage Neuron Networks,” in Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019), pp. 480-484, 2020.

H. Zhi, and S. Liu, “Face recognition based on genetic algorithm,” Journal of Visual Communication and Image Representation, vol. 58, pp.495-502, 2019

T.A. Mohammed, A. Alazzawi, O.N. Uçan, and O. Bayat, “Neural network behavior analysis based on transfer functions MLP & RB in face recognition,” In Proceedings of the First International Conference on Data Science, E-learning and Information Systems, pp. 1-6, 2018.

H. Zhang, A. Jolfaei, and M. Alazab, “A face emotion recognition method using convolutional neural network and image edge computing,” IEEE Access, vol. 7, pp. 159081–159089, 2019.

K. Bahreini, W. Van der Vegt, and W. Westera, “A fuzzy logic approach to reliable real-time recognition of facial emotions,” Multimedia Tools and Applications, vol. 78, no. 14, pp.18943-18966, 2019

A. Adeyanju, E. O. Omidiora, and O. F. Oyedokun, “Performance evaluation of different support vector machine kernels for face emotion recognition,” IntelliSys 2015 - Proc. 2015 SAI Intell. Syst. Conf., pp. 804–806, 2015. doi: 10.1109/IntelliSys.2015.7361233.

G. Sharma, “CK+48 5 Emotions,” 2019. [Online]. Available: https://www.kaggle.com/datasets/gauravsharma99/ck48-5-emotions.

J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, and T. Chen, “Recent advances in convolutional neural networks,” Pattern Recognition, vol. 77, pp.354-377, 2018.

S. Dwijayanti, R.R. Abdillah, H. Hikmarika, Z. Husin, and B.Y. Suprapto, “Facial Expression Recognition and Face Recognition Using a Convolutional Neural Network,” In 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pp. 621-626, 2020.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks, “Advances in neural information processing systems, vol. 25, 2012.

Published
2022-10-06
How to Cite
[1]
M. Iqbal, B. Suprapto, H. Hikmarika, H. Hermawati, and S. Dwijayanti, “Design of Real-Time Face Recognition and Emotion Recognition on Humanoid Robot Using Deep Learning”, JurnalEcotipe, vol. 9, no. 2, pp. 149-158, Oct. 2022.
Abstract viewed = 72 times
PDF downloaded = 42 times