Abstract
The problem of determining people’s age is a recurring theme in areas such as law enforcement, education and sports because age is often used to determine eligibility. The aim of current work is to make use of a lightweight machine learning model for automating the task of detecting people’s age. This paper presents a solution that makes use of a lightweight Convolutional Neural Network model, built according to a modification of the LeNet-5 architecture to perform age detection, for both males and females, in real-time. The UTK-Face Large Scale Face Dataset was used to train and test the performance of the model in terms of predicting age. To evaluate the model’s performance in real-time, Haar Cascades were used to detect faces from video feeds. The detected faces were fed to the model for it to make age predictions. Experimental results showed that age-detection can be performed in real-time. Although, the prediction accuracy of the model requires improvement.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Codella, N.C.F., Lin, C.-C., Halpern, A., Hind, M., Feris, R., Smith, J.R.: Collaborative Human-AI (CHAI): evidence-based interpretable melanoma classification in dermoscopic images. In: Stoyanov, D., et al. (eds.) MLCN/DLF/IMIMIC -2018. LNCS, vol. 11038, pp. 97–105. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02628-8_11
Dodge, S., Karam, L.: A Study and Comparison of Human and Deep Learning Recognition Performance Under Visual Distortions (2017)
Bianco, S.: Large Age-Gap face verification by feature injection in deep networks. Pattern Recogn. Lett. 90, 36–42 (2016)
Howard, A.G., et al.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. Comput. Res. Repository 1704(04861) (2017)
Chen, L., Qian, T., Wang, F., You, Z., Peng, Q., Zhong, M.: Age detection for chinese users in Eeibo. In: Dong, X.L., Yu, X., Li, J., Sun, Y. (eds.) WAIM 2015. LNCS, vol. 9098, pp. 83–95. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21042-1_7
Bae, I.-H.: A rough set based anomaly detection scheme considering the age of user profiles. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4490, pp. 558–561. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72590-9_78
Wu, Y., Li, J., Kong, Y., Fu, Y.: Deep convolutional neural network with independent softmax for large scale face recognition. In: Proceedings of the 24th ACM International Conference on Multimedia, New York (2016)
Ayyadevara, V.K.: Convolutional neural network. In: Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R, pp. 179–215. Apress, Berkely (2018)
Wang, H., Wei, X., Sanchez, V., Li, C.: Fusion network for face-based age estimation. In: 2018 25th IEEE International Conference on Image Processing (ICIP) (2018)
Aydogdu, M.F., Demirci, M.F.: Age Classification using an optimized CNN architecture. In: Proceedings of the International Conference on Compute and Data Analysis, Lakeland (2017)
Cho, J.H., Jang, D., Park, R.: Age category estimation using matching convolutional neural network. In: 2018 IEEE International Conference on Consumer Electronics (ICCE) (2018)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (2018)
Pham, V.H., Dinh, P.Q., Nguyen, V.H.: CNN-based character recognition for license plate recognition system. In: Nguyen, N.T., Hoang, D.H., Hong, T.-P., Pham, H., Trawiński, B. (eds.) ACIIDS 2018. LNCS (LNAI), vol. 10752, pp. 594–603. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75420-8_56
LeCun, Y., Haffner, P., Bottou, L., Bengio, Y.: Object recognition with gradient-based learning. Shape, Contour and Grouping in Computer Vision. LNCS, vol. 1681, pp. 319–345. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-46805-6_19
Ma, M., Gao, Z., Wu, J., Chen, Y., Zheng, X.: A smile detection method based on improved LeNet-5 and support vector machine. In: 2018 IEEE Smartworld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (2018)
Zhang, Z., Song, Y., Qi, H.: Age Progression/Regression by Conditional Adversarial Autoencoder. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Sithungu, S., Van der Haar, D. (2019). Real-Time Age Detection Using a Convolutional Neural Network. In: Abramowicz, W., Corchuelo, R. (eds) Business Information Systems. BIS 2019. Lecture Notes in Business Information Processing, vol 354. Springer, Cham. https://doi.org/10.1007/978-3-030-20482-2_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-20482-2_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20481-5
Online ISBN: 978-3-030-20482-2
eBook Packages: Computer ScienceComputer Science (R0)