Active Shape Model with Multiple Classifiers for Age Prediction

  • Authors

    • Fatma Susilawati Mohamad
    • Musab Iqtait
    https://doi.org/10.14419/ijet.v7i3.28.28426
  • Age Prediction, Feature Extraction, Active Appearance Models (AAM), Age Classification.

  • Abstract

    Automatic age prediction from facial images has received much attention. This is due to its various applications in security control, law enforcement, and human computer interaction. In spite of its developments, age prediction becomes more challenging. This is because the facial age procedure is specified not only by internal factors like genetic factors and external factors like lifestyle and environ-mental factors. In this paper, an enhanced age prediction algorithm using Active Shape Model (ASM) with six classifiers is suggested. Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Canonical Correlation Analysis (CCA), Linear Discriminant Analysis (LDA), and Projection Twin Support Vector Machine (PTSVM) are adopted in order to enhance the accuracy of age prediction. In this work, traits of the facial images are extracted via ASM as the trait vector. The classifiers are utilized and compared to predict the age. The experimental results indicated that CCA has the highest accuracy while KNN has the lowest.

     

  • References

    1. [1] Dong, Y., Y. Liu, and S. Lian, Automatic age estimation based on deep learning algorithm. Neurocomputing, 2016. 187: p. 4-10.

      [2] Liu, K.-H., S. Yan, and C.-C.J. Kuo, Age estimation via grouping and decision fusion. IEEE Transactions on Information Forensics and Security, 2015. 10(11): p. 2408-2423.

      [3] Cootes, T.F., et al., Active shape models-their training and application. Computer vision and image understanding, 1995. 61(1): p. 38-59.

      [4] Cootes, T.F., G.J. Edwards, and C.J. Taylor, Active appearance models. IEEE Transactions on pattern analysis and machine intelligence, 2001. 23(6): p. 681-685.

      [5] Fu, Y. and T.S. Huang, Human age estimation with regression on discriminative aging manifold. IEEE Transactions on Multimedia, 2008. 10(4): p. 578-584.

      [6] Geng, X., et al. Learning from facial aging patterns for automatic age estimation. in Proceedings of the 14th ACM international conference on Multimedia. 2006. ACM.

      [7] Kwon, Y.H. and N. da Vitoria Lobo, Age classification from facial images. Computer vision and image understanding, 1999. 74(1): p. 1-21.

      [8] Niu, Z., et al. Ordinal regression with multiple output cnn for age estimation. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

      [9] Pontes, J.K., et al., A flexible hierarchical approach for facial age estimation based on multiple features. Pattern Recognition, 2016. 54: p. 34-51.

      [10] [Guo, G. and G. Mu. Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression. in Computer vision and pattern recognition (cvpr), 2011 ieee conference on. 2011. IEEE.

      [11] Guo, G. and G. Mu. Joint estimation of age, gender and ethnicity: CCA vs. PLS. in Automatic face and gesture recognition (fg), 2013 10th ieee international conference and workshops on. 2013. IEEE.

      [12] Geng, X., Q. Wang, and Y. Xia. Facial age estimation by adaptive label distribution learning. in Pattern Recognition (ICPR), 2014 22nd International Conference on. 2014. IEEE.

      [13] Iqtait, M., F. Mohamad, and M. Mamat. Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM). in IOP Conference Series: Materials Science and Engineering. 2018. IOP Publishing.

      [14] DibeklioÄŸlu, H., et al., Combining facial dynamics with appearance for age estimation. IEEE Transactions on Image Processing, 2015. 24(6): p. 1928-1943.

      [15] Lai, D., et al., Age estimation with dynamic age range. Multimedia Tools and Applications, 2017. 76(5): p. 6551-6573.

      [16] Mohamad, F.S., M. Iqtait, and F. Alsuhimat. Age prediction on face features via multiple classifiers. in 2018 4th International Conference on Computer and Technology Applications (ICCTA). 2018. IEEE.

      [17] Sai, P.-K., J.-G. Wang, and E.-K. Teoh, Facial age range estimation with extreme learning machines. Neurocomputing, 2015. 149: p. 364-372.

      [18] Ricanek, K. and T. Tesafaye. Morph: A longitudinal image database of normal adult age-progression. in Automatic Face and Gesture Recognition, 2006. FGR 2006. 7th International Conference on. 2006. IEEE.

      [19] Cover, T. and P. Hart, Nearest neighbor pattern classification. IEEE transactions on information theory, 1967. 13(1): p. 21-27.

      [20] Burges, C.J., A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 1998. 2(2): p. 121-167.

      [21] Smola, A.J. and B. Schölkopf, A tutorial on support vector regression. Statistics and computing, 2004. 14(3): p. 199-222.

      [22] Yan, Y., et al., Multitask linear discriminant analysis for view invariant action recognition. IEEE Transactions on Image Processing, 2014. 23(12): p. 5599-5611.

      [23] Hotelling, H., Relations between two sets of variates. Biometrika, 1936. 28(3/4): p. 321-377.

      [24] Chen, X., et al., Recursive projection twin support vector machine via within-class variance minimization. Pattern Recognition, 2011. 44(10-11): p. 2643-2655.

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  • How to Cite

    Susilawati Mohamad, F., & Iqtait, M. (2018). Active Shape Model with Multiple Classifiers for Age Prediction. International Journal of Engineering & Technology, 7(3.28), 329-333. https://doi.org/10.14419/ijet.v7i3.28.28426

    Received date: 2019-03-15

    Accepted date: 2019-03-15