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2023 | OriginalPaper | Chapter

Novel Deep Learning Techniques to Design the Model and Predict Facial Expression, Gender, and Age Recognition

Authors : N. Sujata Gupta, Saroja Kumar Rout, Viyyapu Lokeshwari Vinya, Koti Tejasvi, Bhargavi Rani

Published in: Intelligent Systems and Machine Learning

Publisher: Springer Nature Switzerland

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Abstract

For computer and human interaction, human facial recognition is crucial. Our goal is to anticipate the expression of a human face, gender, and age as quickly and accurately as possible in real-time. Understanding human behavior, detecting mental diseases, and creating synthetic human expressions are only a few of the applications of automatic human facial recognition . Salespeople can employ age, gender, and emotional state prediction to help them better understand their consumers. Convolutional Neural Network one of the Deep Learning techniques is utilized to design the model and predict emotion, age, and gender, using the Haar-Cascade frontal face algorithm to detect the face. This model can predict from video in real-time. The goal is to create a web application that uses a camera to capture a live human face and classify it into one of seven expressions, two ages, and eight age groups. The process of detecting face, pre-processing, feature extraction, and the prediction of expression, gender, and age is carried out in steps.

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Literature
1.
go back to reference Jung, H.: January. Development of deep learning-based facial expression recognition system. In: 2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV), pp. 1–4. IEEE (2015) Jung, H.: January. Development of deep learning-based facial expression recognition system. In: 2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV), pp. 1–4. IEEE (2015)
2.
go back to reference Kwong, J.C.T., Garcia, F.C.C., Abu, P.A.R., Reyes, R.S.: Emotion recognition via facial expression: utilization of numerous feature descriptors in different machine learning algorithms. In: TENCON 2018–2018 IEEE Region 10 Conference, pp. 2045–2049. IEEE (2018) Kwong, J.C.T., Garcia, F.C.C., Abu, P.A.R., Reyes, R.S.: Emotion recognition via facial expression: utilization of numerous feature descriptors in different machine learning algorithms. In: TENCON 2018–2018 IEEE Region 10 Conference, pp. 2045–2049. IEEE (2018)
3.
go back to reference Sambar, M.: FER-2013Dataset. IEEE Access 8 (2020) Sambar, M.: FER-2013Dataset. IEEE Access 8 (2020)
5.
go back to reference Qi, C., et al.: Facial expressions recognition based on cognition and mapped binary patterns. IEEE Access 6, 18795–18803 (2018)CrossRef Qi, C., et al.: Facial expressions recognition based on cognition and mapped binary patterns. IEEE Access 6, 18795–18803 (2018)CrossRef
6.
go back to reference Ali, G., et al.: Artificial neural network based ensemble approach for multicultural facial expressions analysis. IEEE Access 8, 134950–134963 (2020)CrossRef Ali, G., et al.: Artificial neural network based ensemble approach for multicultural facial expressions analysis. IEEE Access 8, 134950–134963 (2020)CrossRef
7.
go back to reference Zhao, H., Wang, P.: A short review of age and gender recognition based on speech. In: 2019 IEEE 5th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS), pp. 183–185. IEEE (2019) Zhao, H., Wang, P.: A short review of age and gender recognition based on speech. In: 2019 IEEE 5th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS), pp. 183–185. IEEE (2019)
8.
go back to reference Ahmed, F., Bari, A.H., Gavrilova, M.L.: Emotion recognition from body movement. IEEE Access 8, 11761–11781 (2019)CrossRef Ahmed, F., Bari, A.H., Gavrilova, M.L.: Emotion recognition from body movement. IEEE Access 8, 11761–11781 (2019)CrossRef
9.
go back to reference Gu, J., et al.: Recent advances in convolutional neural networks. Pattern Recogn. 77, 354–377 (2018)CrossRef Gu, J., et al.: Recent advances in convolutional neural networks. Pattern Recogn. 77, 354–377 (2018)CrossRef
10.
go back to reference Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 international conference on engineering and technology (ICET), pp. 1–6. IEEE (2017) Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 international conference on engineering and technology (ICET), pp. 1–6. IEEE (2017)
11.
go back to reference Tian, Y.: Artificial intelligence image recognition method based on convolutional neural network algorithm. IEEE Access 8, 125731–125744 (2020)CrossRef Tian, Y.: Artificial intelligence image recognition method based on convolutional neural network algorithm. IEEE Access 8, 125731–125744 (2020)CrossRef
12.
go back to reference Howse, J., Joshi, P., Beyeler, M.: OpenCV: Computer Vision Projects with Python. Packt Publishing Ltd. (2016) Howse, J., Joshi, P., Beyeler, M.: OpenCV: Computer Vision Projects with Python. Packt Publishing Ltd. (2016)
13.
go back to reference Shilkrot, R., Escrivá, D.M.: Mastering OpenCV 4: a comprehensive guide to building computer vision and image processing applications with C++. Packt Publishing Ltd. (2018) Shilkrot, R., Escrivá, D.M.: Mastering OpenCV 4: a comprehensive guide to building computer vision and image processing applications with C++. Packt Publishing Ltd. (2018)
14.
go back to reference Hung, J., et al.: Keras R-CNN: library for cell detection in biological images using deep neural networks. BMC Bioinformat. 21(1), 1–7 (2020)CrossRef Hung, J., et al.: Keras R-CNN: library for cell detection in biological images using deep neural networks. BMC Bioinformat. 21(1), 1–7 (2020)CrossRef
15.
go back to reference Levi, G., Hassner, T.: Age and gender classification using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 34–42 (2015) Levi, G., Hassner, T.: Age and gender classification using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 34–42 (2015)
Metadata
Title
Novel Deep Learning Techniques to Design the Model and Predict Facial Expression, Gender, and Age Recognition
Authors
N. Sujata Gupta
Saroja Kumar Rout
Viyyapu Lokeshwari Vinya
Koti Tejasvi
Bhargavi Rani
Copyright Year
2023
DOI
https://doi.org/10.1007/978-3-031-35081-8_29

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