Age Estimation from Facial Images Using Custom Convolutional Neural Network (CNN)


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Authors

  • Gilbert George Nile University
  • Steve Adeshina Nile University
  • Moussa Mahamat Boukar Nile University

Keywords:

Artificial Neural Network, Computer Vison, Convolutional Neural Network, Facial Age Classification

Abstract

Given that aging is influenced by a variety of factors, including gender, ethnicity, environment, and others, automatic age assessment of facial images is a difficult challenge in computer vision and image analysis. Additionally, a significant amount of data and a laborious training phase are needed to estimate age from facial photos with near accuracy. In this study, we present a custom convolutional neural network-based age estimator that can almost precisely predict age from facial photos. We use the UTK facial image dataset using about 17475 images. We train the model to group the facial images into three groups which are; Child, Teenager and Adult. Compared to similar efforts, our method uses less training data while maintaining a high accuracy of 95%.

Author Biographies

Gilbert George, Nile University

Department of Computer Science, Nigeria

Steve Adeshina, Nile University

Department of Engineering, Nigeria

Moussa Mahamat Boukar, Nile University

Department of Computer Science, Nigeria

References

F. Dornaika, S. E. Bekhouche, and I. Arganda-Carreras, “Robust regression with deep CNNs for facial age estimation: An empirical study,” Expert Syst Appl, vol. 141, Mar. 2020, doi: 10.1016/J.ESWA.2019.112942.

O. Agbo-Ajala and S. Viriri, “Deep learning approach for facial age classification: a survey of the state-of-the-art,” Artificial Intelligence Review 2020 54:1, vol. 54, no. 1, pp. 179–213, Jun. 2020, doi: 10.1007/S10462-020-09855-0.

A. Singh, N. Rai, P. Sharma, P. Nagrath, and R. Jain, “Age, Gender Prediction and Emotion recognition using Convolutional Neural Network.” [Online]. Available: https://ssrn.com/abstract=3833759

D. Yi, Z. Lei, and S. Z. Li, “Age estimation by multi-scale convolutional network,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9005, pp. 144–158, 2015, doi: 10.1007/978-3-319-16811-1_10/COVER.

Z. He et al., “Data-Dependent Label Distribution Learning for Age Estimation,” IEEE Transactions on Image Processing, vol. 26, no. 8, 2017, doi: 10.1109/TIP.2017.2655445.

E. Koutsoupias and D. S. Taylor, “The CNN problem and other k -server variants,” Theor Comput Sci, vol. 324, no. 2-3 SPEC. ISS., 2004, doi: 10.1016/j.tcs.2004.06.002.

T. J. Jun et al., “TRk-CNN: Transferable Ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes,” Expert Syst Appl, vol. 182, 2021, doi: 10.1016/j.eswa.2021.115211.

S. Chen, C. Zhang, and M. Dong, “Deep Age Estimation: From Classification to Ranking,” IEEE Trans Multimedia, vol. 20, no. 8, 2018, doi: 10.1109/TMM.2017.2786869.

B. bin Gao, C. Xing, C. W. Xie, J. Wu, and X. Geng, “Deep Label Distribution Learning with Label Ambiguity,” IEEE Transactions on Image Processing, vol. 26, no. 6, 2017, doi: 10.1109/TIP.2017.2689998.

R. Zheng, S. Zhang, L. Liu, Y. Luo, and M. Sun, “Uncertainty in Bayesian deep label distribution learning,” Appl Soft Comput, vol. 101, 2021, doi: 10.1016/j.asoc.2020.107046.

I. Domingues, “An automatic mammogram system: from screening to diagnosis,” Ph.D. Thesis, no. July, 2014.

J. Prajapati, A. Patel, and P. Raninga, “Facial Age Group Classification,” IOSR Journal of Electronics and Communication Engineering, vol. 9, no. 1, pp. 33–39, 2014, doi: 10.9790/2834-09123339.

A. Bearman and C. Dong, “Human Pose Estimation and Activity Classification Using Convolutional Neural Networks,” Stanford CS231n, 2015.

B. Zhang and Y. Bao, “Age Estimation of Faces in Videos Using Head Pose Estimation and Convolutional Neural Networks,” Sensors 2022, Vol. 22, Page 4171, vol. 22, no. 11, p. 4171, May 2022, doi: 10.3390/S22114171.

Y. Nam and C. Lee, “Cascaded convolutional neural network architecture for speech emotion recognition in noisy conditions,” Sensors, vol. 21, no. 13, 2021, doi: 10.3390/s21134399.

A. A. Ahmed and M. Echi, “Hawk-Eye: An AI-Powered Threat Detector for Intelligent Surveillance Cameras,” IEEE Access, vol. 9, 2021, doi: 10.1109/ACCESS.2021.3074319.

I. Ul Haq, A. Ullah, K. Muhammad, M. Y. Lee, and S. W. Baik, “Personalized Movie Summarization Using Deep CNN-Assisted Facial Expression Recognition,” Complexity, vol. 2019, 2019, doi: 10.1155/2019/3581419.

O. Agbo-Ajala and S. Viriri, “Deeply Learned Classifiers for Age and Gender Predictions of Unfiltered Faces,” Scientific World Journal, vol. 2020, 2020, doi: 10.1155/2020/1289408.

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Published

2023-02-10

How to Cite

George, G., Adeshina, S., & Boukar, M. M. (2023). Age Estimation from Facial Images Using Custom Convolutional Neural Network (CNN). International Conference on Frontiers in Academic Research, 1, 134–137. Retrieved from https://as-proceeding.com/index.php/icfar/article/view/41