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Comparative Evaluation on Supervised Learning Based Age Estimation

A. Annie Micheal1 , P. Geetha2 , A. Saranya3

Section:Research Paper, Product Type: Journal Paper
Volume-06 , Issue-07 , Page no. 13-18, Sep-2018

Online published on Sep 30, 2018

Copyright © A. Annie Micheal, P. Geetha , A. Saranya . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: A. Annie Micheal, P. Geetha , A. Saranya, “Comparative Evaluation on Supervised Learning Based Age Estimation,” International Journal of Computer Sciences and Engineering, Vol.06, Issue.07, pp.13-18, 2018.

MLA Style Citation: A. Annie Micheal, P. Geetha , A. Saranya "Comparative Evaluation on Supervised Learning Based Age Estimation." International Journal of Computer Sciences and Engineering 06.07 (2018): 13-18.

APA Style Citation: A. Annie Micheal, P. Geetha , A. Saranya, (2018). Comparative Evaluation on Supervised Learning Based Age Estimation. International Journal of Computer Sciences and Engineering, 06(07), 13-18.

BibTex Style Citation:
@article{Micheal_2018,
author = {A. Annie Micheal, P. Geetha , A. Saranya},
title = {Comparative Evaluation on Supervised Learning Based Age Estimation},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2018},
volume = {06},
Issue = {07},
month = {9},
year = {2018},
issn = {2347-2693},
pages = {13-18},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=459},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=459
TI - Comparative Evaluation on Supervised Learning Based Age Estimation
T2 - International Journal of Computer Sciences and Engineering
AU - A. Annie Micheal, P. Geetha , A. Saranya
PY - 2018
DA - 2018/09/30
PB - IJCSE, Indore, INDIA
SP - 13-18
IS - 07
VL - 06
SN - 2347-2693
ER -

           

Abstract

Facial age estimation has got more consideration in the area of computer vision for the past few years. Age estimation is a troublesome task since the distinction between facial pictures with age variations is difficult. In this work, we analyze the problem of age prediction by means of SVR Model and deep learning technique. This paper attempts to find out the efficiency of SVR and Convolution neural network (CNN) on age estimation. Local features such as wrinkles and texture are extracted using Gabor filter, Local Binary Pattern (LBP) and Local Phase Quantization (LPQ). The three features are combined together and the dimension of the feature vector is reduced using Principle Component Analysis. Support Vector Regression (SVR) is utilized to predict the age of an individual. In CNN, the datasets are fine-tuned utilizing the pre-trained VGG-16 model which can group pictures into 1000 categories. The experimental results on the IMDB-WIKI dataset, the ICCV datasets and MORPH 2 dataset shows that CNN outperforms the local feature based SVR model in predicting the age.

Key-Words / Index Term

Convolutional Neural Network, Local binary Pattern, Local Phase Quantization, Gabor Filter, Support Vector Regression

References

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