Real-time Human Age Estimation based on Facial Images using Uniform Local Binary Patterns

Mohamed Selim, Shekhar Raheja, Didier Stricker

2015

Abstract

This paper summarizes work done on real-time human age-group estimation based on frontal facial images. Our approach relies on detecting visible ageing effects, such as facial skin texture. This information is described using uniform Local Binary Patterns (LBP) and the estimation is done using the K-Nearest Neighbour classifier. In the current work, the system is trained using the FERET dataset. The training data is divided into five main age groups. Facial images captured in real-time using the Microsoft Kinect RGB data are used to classify the subjects age into one of the five different age groups. An accuracy of 81% was achieved on the live testing data. In the proposed approach, only facial regions affected by the ageing process are used in the face description. Moreover, the use of uniform Local Binary Patterns is evaluated in the context of facial description and age-group estimation. Results show that the uniform LBP depicts most of the facial texture information. That led to speeding up the entire process as the feature vector’s length has been reduced significantly, which optimises the process for real-time applications.

References

  1. Fard, H. M., Khanmohammadi, S., Ghaemi, S., and Samadi, F. (2013). Human age-group estimation based on anfis using the hog and lbp features.
  2. Gunay, A. and Nabiyev, V. (2008). Automatic age classification with lbp. In Computer and Information Sciences, 2008. ISCIS 7808. 23rd International Symposium on, pages 1-4.
  3. Hewahi, N., Olwan, C., Tubeel, N., EL-Asar, S., and AbuSultan, Z. (2010). Age estimation based on neural networks using face features. Journal of Emerging Trends in Computing and Information Science, 1(2):61 - 67.
  4. Karthigayani, P. and Sridhar, S. (2011). A novel approach for face recognition and age estimation using local binary pattern, discriminative approach using two layered back propagation network. In Trendz in Information Sciences and Computing (TISC), 2011 3rd International Conference on, pages 11-16.
  5. Kohail, S. (2012). Using artificial neural network for human age estimation based on facial images. In Innovations in Information Technology (IIT), 2012 International Conference on, pages 215-219.
  6. Lanitis, A., Draganova, C., and Christodoulou, C. (2004). Comparing different classifiers for automatic age estimation. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 34(1):621-628.
  7. Microsoft (2012). Kinect. http://www.microsoft.com/enus/kinectforwindows/.
  8. Phillips, P. J., Moon, H., Rizvi, S. A., and Rauss, P. J. (2000). The feret evaluation methodology for facerecognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell., 22(10):1090-1104.
  9. Viola, P. and Jones, M. (2001). Robust real-time object detection. In International Journal of Computer Vision.
  10. Wang, H., Li, S., and Wang, Y. (2004). Face recognition under varying lighting conditions using self quotient image. In Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on, pages 819-824.
  11. Ylioinas, J., Hadid, A., and Pietikainen, M. (2012). Age classification in unconstrained conditions using lbp variants. In Pattern Recognition (ICPR), 2012 21st International Conference on, pages 1257-1260.
  12. Zhao, G. and Pietikäinen, M. (2007). Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell., 29(6):915-928.
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Paper Citation


in Harvard Style

Selim M., Raheja S. and Stricker D. (2015). Real-time Human Age Estimation based on Facial Images using Uniform Local Binary Patterns . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 408-415. DOI: 10.5220/0005311604080415


in Bibtex Style

@conference{visapp15,
author={Mohamed Selim and Shekhar Raheja and Didier Stricker},
title={Real-time Human Age Estimation based on Facial Images using Uniform Local Binary Patterns},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={408-415},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005311604080415},
isbn={978-989-758-090-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Real-time Human Age Estimation based on Facial Images using Uniform Local Binary Patterns
SN - 978-989-758-090-1
AU - Selim M.
AU - Raheja S.
AU - Stricker D.
PY - 2015
SP - 408
EP - 415
DO - 10.5220/0005311604080415