Facial Emotion Based Face Emoji Generation
Keywords:
Emoticons, Human-Computer Interaction, Facial Expression Recognition, Machine Learning, Deep learning.Abstract
Face expressions are an intrinsic aspect of
nonverbal communication and play a significant role
in Human Computer Interaction. The formation of
facial emoticons is a human-computer interaction
device. Emoji generation in real time based on a
person's facial expression hasalways been difficult.
Human social conversations depend on facial
expressions. Since the world is becoming more
technologically sophisticated day in day out, there
are more interactive encounters, such as text
messages, than physical ones. Emoticons promote
virtual social interaction by reducing the amount of
words exchanged. This paper describes an Emoji
generation methodology based on Facial Expression
Recognition (FER) and Convolutional Neural
Networks (CNN) coupled with Machine Learning
and Deep Learning. This CNN-based model can be
put to work to evaluate feelings as users watch movie
trailers or video lessons, as well as to help people with
autism regulate their emotions.
Downloads
References
Alshamsi, Humaid, Veton Kepuska, and Hongying Meng.
"Real time automated facial expression recognition app
development on smart phones." In 2017 8th IEEE Annual
Information Technology, Electronics and Mobile
Communication Conference (IEMCON), pp. 384-392. IEEE,
2017.
[2] Ekman .P & Keltner, D Universal facial expressions of
emotion: An old controversy and new findings. In U. C.
Segerstråle & P. Molnár (Eds.), Nonverbal communication:
Where nature meets culture (pp. 27–46). Lawrence Erlbaum
Associates, Inc. 1997.
[3] Fathallah, Abir, Lotfi Abdi, and Ali Douik. "Facial
expression recognition via deep learning." In 2017 IEEE/ACS
14th International Conference on Computer Systems and
Applications (AICCSA), pp. 745-750. IEEE, 2017.
[4] Goodfellow, Ian J., Yaroslav Bulatov, Julian Ibarz, Sacha
Arnoud, and Vinay Shet. "Multi-digit number recognition from
street view imagery using deep convolutional neural networks."
arXiv preprint arXiv:1312.6082, 2013.
[5] McDuff, D., Mahmoud, A., Mavadati, M., Amr, M., Turcot,
J., & Kaliouby, R. E. (2016, May). AFFDEX SDK: a crossplatform
realtime multi-face expression recognition toolkit. In
Proceedings of the CHI conference extended abstracts on human
factors in computing systems (pp. 3723-3726). ACM, 2016.
[6] J. Chen, Y. Lv, R. Xu, and C. Xu, "Automatic social signal
analysis: Facial expression recognition using difference
convolution neural network," Journal of Parallel and Distributed
Computing, vol. 131, pp. 97-102, 2019.
[7] Barsoum, Emad, et al, Training deep networks for facial
expression recognition with crowdsourced label distribution,
ACM International Conference on Multimodal Interaction
ACM, pp. 279—283, 2016.
[8] Martinez, Brais, et al, Automatic analysis of facial actions: A
survey, IEEE Transactions on Affective Computing, 2017.
[9] H.-D. Nguyen, S. Yeom, G.-S. Lee, H.-J. Yang, I. Na, and S.
H. Kim, "Facial Emotion Recognition Using an Ensemble of
MultiLevel Convolutional Neural Networks," International
Journal of Pattern Recognition and Artificial Intelligence, 2018.
[10] T. Cao and M. Li, "Facial Expression Recognition
Algorithm Based on the Combination of CNN and K-Means,"
presented at the Proceedings of the 2019 11th International
Conference on Machine Learning and Computing, Zhuhai,
China, 2019.
[11] N. Christou and N. Kanojiya, "Human Facial Expression
Recognition with Convolutional Neural Networks," Singapore,
2019, pp. 539-545: Springer Singapore
[12] A. Sajjanhar, Z. Wu, and Q. Wen, "Deep learning models
for facial expression recognition," in 2018 Digital Image
Computing: Techniques and Applications (DICTA), 2018, pp.
1-6: I EEE.
[13] J. Chen, Y. Lv, R. Xu, and C. Xu, "Automatic social signal
analysis: Facial expression recognition using difference
convolution neural network," Journal of Parallel and Distributed
Computing, vol. 131, pp. 97-102, 2019.
[14]Al-Sumaidaee, Saadoon AM, et al, Multi-gradient features
and elongated quinary pattern encoding for image-based facial
expression recognition, Pattern Recognition, 2017, pp. 249—
263.
[15] Barsoum, Emad, et al, Training deep networks for facial
expression recognition with crowd-sourced label distribution,
ACM International Conference on Multimodal Interaction
ACM, 2016, pp. 279—283.