Logo PTI Logo FedCSIS

Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 32

Face and silhouette based age estimation for child detection system

, ,

DOI: http://dx.doi.org/10.15439/2022F98

Citation: Communication Papers of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 32, pages 3943 ()

Full text

Abstract. The problem of age estimation based on facial imagesis a well-known computer vision task that is widely applied in identification systems. With help of the special Dyzurnet.pl unit detecting Internet content, related to sexual abuse of children we slightly redefined a problem. Our Convolutional Neural Network (CNN) solution is focused on infants and prepubescents recognition and in the particular age ranges can be considered as the-state-of-the-art in children detection. Silhouette-based age estimation is often concentrated on the human gait or body proportions analysis. Single image age estimations on the dressed (fully or partly) body are not typically researched because of a lack of properly labeled data. In our work, we present the method used to train image preparation and the final effectiveness of age estimation of that kind. The proposed solution is a part of the system for responding to threats to children's safety in cyberspace with particular emphasis on child pornography.


  1. R. Rothe, R. Timofte, L. Van Gool. 2015. “DEX: Deep EXpectation of Apparent Age from a Single Image”. 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), pp. 252-257. http://dx.doi.org/10.1109/IC-CVW.2015.41.
  2. H. Pan, H. Han, S. Shan, X. Chen. 2018. “Mean-Variance Loss for Deep Age Estimation from a Face”. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5285-5294. http://dx.doi.org/10.1109/CVPR.2018.00554.
  3. Y. Tingting, W. Junqian, W. Lintai, X. Yong. 2019. “Three-stage network for age estimation”. 2019. CAAI Transactions on Intelligence Technology. http://dx.doi.org/10.1049/trit.2019.0017
  4. G. Levi, T. Hassner. 2015. “Age and Gender Classification Using Convolutional Neural Networks”. IEEE Workshop on Analysis and Modeling of Faces and Gestures (AMFG) at the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). http://dx.doi.org/10.1109/cvprw.2015.7301352
  5. Y. Ge, J. Lu, W. Fan and D. Yang. 2013. “Age estimation from human body images”. 2013 IEEE Conference on Acoustics. Seech and Signal Processing. http://dx.doi.org/10.1109/ICASSP.2013.6638072
  6. O. F. Ince, J. Park, J. Song, B. Yoon. 2014. “Child and Adult Classification Using Ratio of Head and Body Heights in Images”. International Journal of Computer and Communication Engineering. http://dx.doi.org/10.7763/IJCCE.2014.V3.3042
  7. O. F. Ince, J. Park, J. Song, B. Yoon. 2021. “Real-Time Gait-Based Age Estimation and Gender Classification from a Single Image”. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). http://dx.doi.org/10.1109/WACV48630.2021.00350
  8. N. Mansouri, M. Aouled Issa, Y. B. Ben Jemaa. 2017. “Gait-based Human Age Classification Using a Silhouette Model”. IET Biometrics. http://dx.doi.org/10.1049/iet-bmt.2016.0176
  9. H. Zhu, Y. Zhang, G. Li, J. Zhang, H. Shan. 2019. “Ordinal Distribution Regression for Gait-based Age Estimation”. SCIENCE CHINA Information Sciences. http://dx.doi.org/10.1007/s11432-019-2733-4
  10. E. Agustsson, R. Timofte, S. Escalera, X. Baro, I. Guyon, R. Rothe, 2017. “Apparent and Real Age Estimation in Still Images with Deep Residual Regressors on Appa-Real Database”. 2017 12th IEEE International Conference on Automatic Face Gesture Recognition (FG 2017), pp. 87-94. http://dx.doi.org/10.1109/FG.2017.20.
  11. Y. Fu, T. M. Hospedales, T. Xiang, J. Xiong, S. Gong, Y. Wang, Y. Yao. 2016. “Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3). 563–577. http://dx.doi.org/10.1109/tpami.2015.2456887
  12. X. Zhu, D. Ramanan. 2012. “Face detection, pose estimation, and landmark localization in the wild”. 2012 IEEE Conference on Computer Vision and Pattern Recognition. pp. 2879-2886. http://dx.doi.org/10.1109/CVPR.2012.6248014.
  13. V. Le, J. Brandt, Z. Lin, L. Bourdev, T. S. Huang. 2012. “Interactive Facial Feature Localization”. Lecture Notes in Computer Science. 679–692. http://dx.doi.org/10.1007/978-3-642-33712-3 49
  14. Y. Zhang, L. Liu, C. Li, C.-C. Loy, Chen Change. 2017. “Quantifying Facial Age by Posterior of Age Comparisons”. Proceedings of the British Machine Vision Conference (BMVC). http://dx.doi.org/10.5244/C.31.108
  15. Zhou, Yijun, Gregson, James. 2020. “WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose”. arXiv. http://dx.doi.org/10.48550/arXiv.2005.10353
  16. X. Zhu, Z. Lei, X. Liu, H. Shi, S. Li. 2016. “Face Alignment Across Large Poses: A 3D Solution”. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 146-155. http://dx.doi.org/10.1109/CVPR.2016.23
  17. J. Redmon, A. Farhadi. 2018. “YOLOv3: An Incremental Improvement”. https://arxiv.org/abs/1804.02767. http://dx.doi.org/10.48550/arXiv.1804.02767
  18. D. King, A. Farhadi. 2009. “Dlib-ml: A machine learning toolkit”. Journal of Machine Learning Research
  19. Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., Le, Q. V. 2019. ”AutoAugment: Learning Augmentation Strategies From Data”. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). http://dx.doi.org/10.1109/cvpr.2019.00020
  20. M. Leppioja, P. Luuka, C. Lohrmann. 2021. “Image based classification of shipments using transfer learning”. Recent Advances in Business Analytics. Selected papers of the 2021 KNOWCON-NSAIS workshop on Business Analytics. ACSIS, Vol. 29. pages 37–44 (2021), http://dx.doi.org/10.15439/2021B4
  21. K. He, X. Zhang, S. Ren, J. Sun. 2016. “Deep Residual Learning for Image Recognition”. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 770-778. http://dx.doi.org/10.1109/CVPR.2016.90.
  22. J. Deng, W. Dong, Socher, R., Li-Jia Li, Kai Li, Li Fei-Fei. 2009. “ImageNet: A large-scale hierarchical image database”. 2009 IEEE Conference on Computer Vision and Pattern Recognition. http://dx.doi.org/10.1109/cvprw.2009.5206848
  23. J. HU, L. Shen, G. sun. 2018. “Squeeze-and-Excitation Networks”. IEEE Conference on Computer Vision and Pattern Recognition. http://dx.doi.org/10.1109/CVPR.2018.00745
  24. D. P. Kingma, J. Ba. 2014. “Adam: A Method for Stochastic Optimization”. Proceedings of the 3rd International Conference on Learning Representations. http://dx.doi.org/10.48550/arXiv.1412.6980