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.

  • Abstract

    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.

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  • 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

    Received date: 2018-01-12

    Accepted date: 2018-02-14

    Published date: 2018-03-13