Efficient technique to estimate age using PCA & multi SVM classification

  • Authors

    • Laiphrakpam Jibanpriya Devi
    • J L Mazher Iqbal
    2017-12-28
    https://doi.org/10.14419/ijet.v7i1.2.8999
  • Facial Images, SVM, PCA Algorithm, Age Estimation, Multi SVM.
  • Human interaction with computer is recent trend in computer technology. In order to obtain age information, image-based age estimation systems have been developed using information from the human facial images. We develop a new technology which identify the characteristic of human being like age. Facial information study will lead us to identify age. While generic growth patterns that are characteristics of different age groups can be identified. In order to create an accurate algorithm for age classification, we build an appropriate datasets for training is build using SVM classification method. We build an application base on MATLAB software to estimate age based on the trained data. Feature of face is extracted using PCA method and stored the data in array matrix. The accuracy of the trained data is 95.65%. We have an average matching percentage of 92%. We have Euclidean distance calculation method to verify the matched data and we found 100% verified.

  • References

    1. [1] Richang Hong,and Shuicheng Yan, Zhenzhen Hu, Yonggang Wen Jianfeng Wang, Meng Wang,†Facial Age Estimation With Age Differenceâ€, IEEE Transactions On Image Processing, Vol. 26, No. 7, July 2017.

      [2] Petra Grd,Miroslav BaÄa,â€Creating a Face Database for Age Estimation and Classificationâ€,MIPRO 2016,Opatija,Croatia,May 30 - June 3, 2016. https://doi.org/10.1109/MIPRO.2016.7522353.

      [3] Julianson Berueco, Kim Lopena, Arby Moay, Mehdi Salemiseresht, and Chuchi Montenegro,“Age Estimation Using Support Vector Machine– Sequential Minimal Optimizationâ€,Journal of Image and Graphics, Volume 2, No.2, December 2014.

      [4] G. Guo and X. Wang, “A study on human age estimation under facial expression changes. In Proc. IEEECVPR, pages 2547–2553, 2012.

      [5] Yu Zhu* 1, Yan Li* 1,2, Guowang Mu1, and Guodong Guo1, “A Study on Apparent Age Estimationâ€,IEEE International Conference on Computer Vision Workshops, 2015.

      [6] Louis Quinn, Margaret Lech, “Multi-Stage Classification Network for Automatic Age Estimation from Facial Imagesâ€, IEEE Transaction, 2015

      [7] Imed Bouchrika, Nouzha,Harrati,Ammar Ladjailia and Sofiane Khedairia,“Age Estimation from facial Images based on Hierarchical Feature Selectionâ€,16th international conference on Sciences andTechniques of Automatic control & computer engineering STA'2015, Monastir, Tunisia, December 21-23, 2015. https://doi.org/10.1109/STA.2015.7505156.

      [8] Young H. Kwon, Niels da Vitoria Lobo,“Age Classification from Facial Images, Computer Vision and Image Understandingâ€,74(1),pp.1–21,1999. https://doi.org/10.1006/cviu.1997.0549.

      [9] Y. Fu, G. Guo, and T. Huang, “Age synthesis and estimation via faces: A surveyâ€, IEEE Trans. PAMI, 32(11):1955 –1976, Nov. 2010. https://doi.org/10.1109/TPAMI.2010.36.

      [10] XinGeng, Zhi-Hua Zhou, Senior Member, IEEE, and Kate Smith- Miles, Senior Member, IEEE,â€Automatic Age Estimation Based on Facial Aging Patternsâ€â€“IEEE Transactions On Pattern Analysis and Machine Intelligence, Vol. 29, No. 12, December 2007.

      [11] Chin-Teng Lin, Dong-Lin Li, Jian-Hao Lai, Ming-Feng Han and Jyh-Yeong Chang, "Automatic Age Estimation System for Face Images" - International Journal of Advanced Robotic Systems, 29 Aug 2012. https://doi.org/10.5772/52862.

      [12] Dr.J L Mazher Iqbal, K. Iswarya Lakshmi, “Object Detection and Tracking Using Thermal Cameraâ€, International Journal of Pure and Applied Mathematics, Volume 114 No. 10 2017, 87-95.

      [13] Dr.J L Mazher Iqbal, Shaik Shakir Basha, ‚Real Time 3D Depth Estimation and Measurement of Un-calibrated Stereo and Thermal Images‛, 2017 International Conference on Nascent Technologies in the Engineering Field (ICNTE-2017), 978-1-5090-2794-1/17/$31.00 ©2017 IEEE.

      [14] J.L. Mazher Iqbal, M. Suriya Parveen, S. Arun, ‚ Image stitching and 2D to 3D Image Reconstruction for Abnormal Activity Detection‛, International Journal of Computer Applications (0975 – 8887) Volume 133 – No.17, January 2016.

      [15] J.L.Mazher Iqbal, J.Lavanya and S.Arun, ‚Abnormal Human Activity Recognition using Scale Invariant Feature Transform‛, International Journal of Current Engineering and Technology, Vol.5, No.6, Dec 2015.

      [16] Ravindraiah, Dr. J. L. Mazher Iqbal (2014), Hard Exudates Detection in Proliferative Diabetic Retinopathy using Gradient Controlled Fuzzy C MEANS Clustering Algorithm, GESJ: Computer Science and Telecommunications,Vol No.4 (44), PP 27-31.

      [17] R. Ravindraiah, Dr. J. L. Mazher Iqbal (2014), Segmentation of Diabetic Retinopathy Images through a Subjective Approach, GESJ: Computer Science and Telecommunications, VolNo.4 (44), PP 32-34, 2014.

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  • How to Cite

    Jibanpriya Devi, L., & Mazher Iqbal, J. L. (2017). Efficient technique to estimate age using PCA & multi SVM classification. International Journal of Engineering & Technology, 7(1.2), 81-84. https://doi.org/10.14419/ijet.v7i1.2.8999