Active Appearance Model for Age Prediction: A Comparison

  • Authors

    • Musab Iqtait
    • Fatma Susilawati Mohamad
    • Fadi Alsuhimat
    2018-10-07
    https://doi.org/10.14419/ijet.v7i4.15.28364
  • Age Prediction, Feature Extraction, Active Appearance Models (AAM), Age Classification.
  • Individual age gives key demographic data. It is viewed as a paramount delicate biometric characteristic for individual identification, contrasted with other pattern recognition issues. Age estimation is a complex issue particularly in relation to facial pictures with different ages, since the aging procedure varies extraordinarily across different age groups. In this work, we investigate deep learning techniques for age prediction based on Active Appearance Models (AAM) and six classifiers: 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) to improve the precision of age prediction based on the present methods. In this algorithm, we extracted the traits of the facial images as traits vectors using AAM model, and the classifiers are utilized to predict the age. We were able to recognize that the accuracy of CCA algorithm is the best, the intermediate is SVR and the KNN algorithm is the lowest.

     

     


  • References

    1. [1] Grd, P., Introduction to human age estimation using face images. Research Papers, Faculty of Materials Science and Technology Slovak University of Technology, 2013. 21(Special Issue): 24-30.

      [2] Geng, X., Q. Wang, and Y. Xia. Facial age estimation by adaptive label distribution learning. Proceedings of the IEEE 22nd International Conference on Pattern Recognition, 2014.

      [3] Wang, X., R. Guo, and C. Kambhamettu. Deeply-learned feature for age estimation. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2015.

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

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

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

      [7] Niu, Z., et al. Ordinal regression with multiple output cnn for age estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.

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

      [9] 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.

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

      [11] Burges, C.J., A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 1998. 2(2): 121-167.

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

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

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

  • Downloads

  • How to Cite

    Iqtait, M., Susilawati Mohamad, F., & Alsuhimat, F. (2018). Active Appearance Model for Age Prediction: A Comparison. International Journal of Engineering & Technology, 7(4.15), 539-543. https://doi.org/10.14419/ijet.v7i4.15.28364