POSITIONAL TERNARY PATTERN FEATURES BASED HUMAN AGE CLASSIFICATION AND ESTIMATION USING ARTIFICIAL NEURAL NETWORK

  • Miss. Shamli Jagzap
  • Prof. J.A. Kendule
Keywords: Facial Recognition, Positional Ternary Pattern, GLCM Feature extraction, Principal Component Analysis, Artificial Neural Network, MATLAB 2014.

Abstract

In this paper, it is proposed to have a method for classification of human age using Artificial Neural Network(ANN) classifier. The classification of human age from facial pictures plays an important role in pc vision, scientific discipline and forensic Science. the various machine and mathematical models, for classifying facial age together with  Principal component Analysis ( PCA), positional Ternary Pattern (PTP) are planned yields higher performance. This paper proposes a completely unique technique of classifying the human age group exploitation Artificial Neural Network. This is often done by pre-processing the face image initially and so extracting the face options exploitation PCA. Then the classification of human age is finished exploitation Artificial Neural Network (ANN). The age is classed into four classes: kid, Young, Middle- aged, and Old. The method of combining PCA and ANN perform higher rather victimization separately.

 

References

[1] E. Eidinger, R. Enbar, and T. Hassner, “Age and gender estimation of unfiltered faces,” Information Forensics and Security, IEEE Transactions on, vol. 9, no. 12, pp. 2170–2179, 2014.

[2] S. E. Choi, Y. J. Lee, S. J. Lee, K. R. Park, and J. Kim, “Age estimation using a hierarchical classifier based on global and local facial features,” Pattern Recognition, vol. 44, no. 6, pp. 1262–1281, 2011.


[3] G. Mahalingam and C. Kambhamettu, “Can discriminative cues aid face recognition across age?” in Proc. IEEE Int. Conf. Autom. Face Gesture Recognit. Workshops, Mar. 2011, pp. 206–212.

[4] G. Guo, Y. Fu, C.R. Dyer, and T.S. Huang, “Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression,” IEEE Trans. Image Processing, vol. 17, no. 7, pp. 1178-1188, July 2008.


[5]Tianyue Zheng; Weihong Deng; Jiani Hu, “Deep probabilities for age estimation” 2017 IEEE Visual Communications and Image Processing (VCIP).


[6] J. Suo, T. Wu, S.C. Zhu, S. Shan, X. Chen, and W. Gao, “Design Sparse Features for Age Estimation Using Hierarchical Face Model,” Proc. Eighth Int’l Conf. Automatic Face and Gesture Recognition, 2008.
Published
2018-11-05
How to Cite
Jagzap, M. S., & Kendule, P. J. (2018, November 5). POSITIONAL TERNARY PATTERN FEATURES BASED HUMAN AGE CLASSIFICATION AND ESTIMATION USING ARTIFICIAL NEURAL NETWORK. ASIAN JOURNAL FOR CONVERGENCE IN TECHNOLOGY (AJCT ) -UGC LISTED, 4(II). https://doi.org/https://doi.org/10.33130/asian%20journals.v4iII.611
Section
Article