Facial age estimation using SFTA and deep neural network

  • Authors

    • Deepa Nagarajan Sathyabama University
    • T. Sasipraba
    2018-03-13
    https://doi.org/10.14419/ijet.v7i2.9166
  • Face Recognition, Image Edge Detection, Image Segmentation, Pattern Recognition, Texture Analysis.

  • This paper construes the toils in facial age estimation in images. The fact that manual age estimation is indeed hard rising out the urge for digital age estimation. To make estimation precise many works have been carried out by considering a lot of constraints. In this paper, facial age estimation is done more accurately. SFTA method is used for feature extraction and meticulous results are obtained for all age groups. Histogram equalization is done using the Otsu algorithm and three layered Deep Neural Network is used to classify the age group. In a Deep neural network, softmax normalization is done in the final layer to preserve the outlier values. By extracting 45 feature values concerning color and gradient, key point descriptor, orientation, shape and texture better estimation are obtained.

  • References

    1. [1] D. T. Nguyen, S. R. Cho and K. R. Park, Human age estimation based on multi-level local binary pattern and regression method in future information technology, vol. 309 of Lecture Notes in Electrical Engineering Springer 2014 pp. 433–438 available online: " https://link.springer.com/chapter/10.1007/978-3-642-55038-6_67".

      [2] Narayanan Ramanathan, Rama Chellappa and Soma Biswas, Computational methods for modeling facial aging: A survey, Journal of Visual Languages and Computing, Elsevier Volume 20, Issue 3, June 2009, Pages 131-144 https://doi.org/10.1016/j.jvlc.2009.01.011.

      [3] Maja Pantic and Leon J.M. Rothkrantz, Automatic analysis of facial expressions: The state of the Art, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 22, No. 12, December 2000.

      [4] Husain Lakdawala, Rehan Mastan, Jibran Patel and Er. Nazneen Pendhari, Analysis of human age estimation process, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, Issue 3, March 2015.

      [5] Zhifeng Li, Unsang Park, and Anil K. Jain, A discriminative model for age invariant face recognition, IEEE Transactions On Information Forensics And Security, Vol. 6, No. 3, September 2011 https://doi.org/10.1109/TIFS.2011.2156787.

      [6] Hamid Moghadam fard, Sohrab Khanmohammadi, Sahraneh Ghaemi and Farshad Samadi, Human age-group estimation based on ANFIS using the HOG and LBP features, Electrical and Electronics Engineering, Vol .2, No 1, February 2013.

      [7] Hlaing Htake, Khaung Tin, Subjective age prediction of face images using PCA, International Journal of Information and Electronics Engineering, Vol.2, No. 3, May 2012.

      [8] Cunjian Chen, Antitza Dantcheva and Arun Ross, Impact of facial cosmetics on automatic gender and age estimation algorithms, Proc. of 9th International Conference on Computer Vision Theory and Applications (VISAPP), (Lisbon, Portugal), January 2014.

      [9] S. Z. Li and A. K. Jain, Handbook of face recognition, Springer Edition, 2011 https://doi.org/10.1007/978-0-85729-932-1.

      [10] C. Liu and H. Wechsler, Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition, IEEE Trans. Image Process. 11(4), pp. 467–476, 2002 https://doi.org/10.1109/TIP.2002.999679.

      [11] R.Venkata Ramana Chary , D.Rajya Lakshmi and K.V.N Sunitha, Feature extraction methods for color image similarity, Advanced Computing, An International Journal ( ACIJ ), Vol.3, No.2, March 2012.

      [12] Andreas Lanitis, Chrisina Draganova, and Chris Christodoulou, Comparing different classifiers for automatic age estimation, IEEE Transactions On Systems, Man, And Cybernetics—Part B: Cybernetics, Vol. 34, No. 1, February 2004 https://doi.org/10.1109/TSMCB.2003.817091.

      [13] Apoorva B., and Santhosh Kumar, Human age estimation by using facial landmarks, International Journal for Scientific Research & Development| Vol. 3, Issue 03, 2015 available online: " http://ijsrd.com/Article.php?manuscript=IJSRDV3I31617".

      [14] X. Tan and B. Triggs, Enhanced local texture feature sets for face recognition under difficult lighting conditions, IEEE Trans. Image Process. 19(6) 2010 pp. 1635–1650 https://doi.org/10.1109/TIP.2010.2042645.

      [15] Aditi Sengupta, and Piyas Mondal, Estimating the age of human face in image processing using Matlab, International Journal Of Engineering And Computer Science ISSN:2319-7242, Vol. 4, Issue 3 March 2015, Page No. 10899-10902 available online: www.ijecs.in/issue/v4-i3/61%20ijecs.pdf.

      [16] Chin-Teng Lin, Dong-Lin Li, Jian-Hao Lai, Ming-Feng Han1 and Jyh-Yeong Chang, Automatic age estimation system for face images, International Journal of Advanced Robotic Systems, vol. 9, August 2012 https://doi.org/10.5772/52862.

      [17] M. Aarthy, and P. Sumathy, A comparison of histogram equalization method and histogram expansion, International Journal of Computer Science and Mobile Applications, Vol.2 Issue. 3, March- 2014, pg. 25-34 available online: "http://www.ijcsma.com/volume2issue3.html".

      [18] Xin Geng, Zhi-Hua Zhou and Kate Smith-Miles, Automatic age estimation based on facial aging patterns, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 29, No. 12, December 2007 https://doi.org/10.1109/TPAMI.2007.70733.

      [19] Jinli Suo, Song-Chun Zhu, Shiguang Shan, and Xilin Chen, A compositional and dynamic model for face aging, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 32, No. 3, March 2010 available online: "https://dl.acm.org/citation.cfm?id=1729472.1729556".

      [20] Nabil Hewahi, Aya Olwan, Nebal Tubeel, Salha EL-Asar, and Zeinab Abu-Sultan, Age estimation based on neural networks using face features, Journal of emerging trends in computing and information Sciences Vol. 1, No. 2, Oct 2010.

  • Downloads

  • How to Cite

    Nagarajan, D., & Sasipraba, T. (2018). Facial age estimation using SFTA and deep neural network. International Journal of Engineering & Technology, 7(2), 281-288. https://doi.org/10.14419/ijet.v7i2.9166