Open Access   Article Go Back

Age and Gender Detection using Deep Learning Models

Arsala Kadri1 , Kirti Sharma2 , Narendrasinh Chauhan3

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-4 , Page no. 671-676, Apr-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i4.671676

Online published on Apr 30, 2019

Copyright © Arsala Kadri, Kirti Sharma, Narendrasinh Chauhan . 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.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Arsala Kadri, Kirti Sharma, Narendrasinh Chauhan, “Age and Gender Detection using Deep Learning Models,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.671-676, 2019.

MLA Style Citation: Arsala Kadri, Kirti Sharma, Narendrasinh Chauhan "Age and Gender Detection using Deep Learning Models." International Journal of Computer Sciences and Engineering 7.4 (2019): 671-676.

APA Style Citation: Arsala Kadri, Kirti Sharma, Narendrasinh Chauhan, (2019). Age and Gender Detection using Deep Learning Models. International Journal of Computer Sciences and Engineering, 7(4), 671-676.

BibTex Style Citation:
@article{Kadri_2019,
author = {Arsala Kadri, Kirti Sharma, Narendrasinh Chauhan},
title = {Age and Gender Detection using Deep Learning Models},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2019},
volume = {7},
Issue = {4},
month = {4},
year = {2019},
issn = {2347-2693},
pages = {671-676},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4097},
doi = {https://doi.org/10.26438/ijcse/v7i4.671676}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i4.671676}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4097
TI - Age and Gender Detection using Deep Learning Models
T2 - International Journal of Computer Sciences and Engineering
AU - Arsala Kadri, Kirti Sharma, Narendrasinh Chauhan
PY - 2019
DA - 2019/04/30
PB - IJCSE, Indore, INDIA
SP - 671-676
IS - 4
VL - 7
SN - 2347-2693
ER -

VIEWS PDF XML
417 340 downloads 167 downloads
  
  
           

Abstract

Computer vision is a field of computer science that works on enabling computers to see, identify and process data in the same way that human vision does, and then provide appropriate output. It is like imparting human intelligence and instincts to a computer. It includes methods for acquiring, processing, analyzing and understanding Videos or Images. The main goal is not only to see, but also process and provide useful results based on the observation. Age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. This research report represents information regarding Age & Gender Detection of a person by using Deep Learning Models and Transfer Learning.

Key-Words / Index Term

Age & Gender Detection, Convolutional Neural Network, Deep Learning, Transfer Learning

References

representation from predicting 10,000 classes. In Proc. Conf. Comput. Vision Pattern Recognition, pages 1891–1898. IEEE, 2014.
[2] Y. H. Kwon and N. da Vitoria Lobo. Age classification from facial images. In Proc. Conf. Comput. Vision Pattern Recognition, pages 762–767. IEEE, 1994.
[3] E. Eidinger, R. Enbar, and T. Hassner. Age and gender estimation of unfiltered faces. Trans. on Inform. Forensics and Security, 9(12), 2014
[4] Y. H. Kwon and N. da Vitoria Lobo. Age classification from facial images. In Proc. Conf. Comput. Vision Pattern Recognition, pages 762–767. IEEE, 1994.
[5] C. Liu and H. Wechsler. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. Trans. Image Processing, 11(4):467–476, 2002.
[6] W.-L. Chao, J.-Z. Liu, and J.-J. Ding. Facial age estimation based on label-sensitive learning and age-oriented regression. Pattern Recognition, 46(3):628–641, 2013.
[7] https://www.kaggle.com/jessicali9530/celeba-dataset
[8]https://www.kaggle.com/ttungl/adience-benchmark-gender-and-age-classification
[9] Felix Anda, David Lillis, Nhien-An Le-Khac, Mark Scanlon, Evaluating Automated Facial Age Estimation Techniques for Digital Forensics, 2018 IEEE Symposium on Security and Privacy Workshops.
[10] Y. Fu, G. Guo, and T. S. Huang. Age synthesis and estimation via faces: A survey. Trans. Pattern Anal. Mach. Intell., 32(11):1955–1976, 2010.
[11] H. Han, C. Otto, and A. K. Jain. Age estimation from face images: Human vs. machine performance. In Biometrics (ICB), 2013 International Conference on. IEEE, 2013.
[12] Y. H. Kwon and N. da Vitoria Lobo. Age classification from facial images. In Proc. Conf. Comput. Vision Pattern Recognition, pages 762–767. IEEE, 1994.
[13] N. Ramanathan and R. Chellappa. Modeling age progression in young faces. In Proc. Conf. Comput. Vision Pattern Recognition, volume 1, pages 387–394. IEEE, 2006.
[14] X. Geng, Z.-H. Zhou, and K. Smith-Miles. Automatic age estimation based on facial aging patterns. Trans. Pattern Anal. Mach. Intell., 29(12):2234–2240, 2007.
[15] G. Guo, Y. Fu, C. R. Dyer, and T. S. Huang. Imagebased human age estimation by manifold learning and locally adjusted robust regression. Trans. Image Processing, 17(7):1178–1188, 2008.
[16] Y. Fu and T. S. Huang. Human age estimation with regression on discriminative aging manifold. Int. Conf. Multimedia, 10(4):578–584, 2008.
[17] A. Lanitis. The FG-NET aging database, 2002. Available: www-prima.inrialpes.fr/FGnet/html/ FERET.html
[18] K. Ricanek and T. Tesafaye. Morph: A longitudinal image database of normal adult age-progression. In Int. Conf. on Automatic Face and Gesture Recognition, pages 341–345. IEEE, 2006.
[19] S. Yan, X. Zhou, M. Liu, M. Hasegawa-Johnson, and T. S. Huang. Regression from patch-kernel. In Proc. Conf. Comput. Vision Pattern Recognition. IEEE, 2008.
[20] K. Fukunaga. Introduction to statistical pattern recognition. Academic press, 1991.
[21] X. Zhuang, X. Zhou, M. Hasegawa-Johnson, and T. Huang. Face age estimation using patch-based hidden markov model supervectors. In Int. Conf. Pattern Recognition. IEEE, 2008.
[22] C. Perez, J. Tapia, P. Est´evez, and C. Held. Gender classification from face images using mutual information and feature fusion. International Journal of Optomechatronics, 6(1):92– 119, 2012.
[23] S. Baluja and H. A. Rowley. Boosting sex identification performance. Int. J. Comput. Vision, 71(1):111–119, 2007.
[24] M. Toews and T. Arbel. Detection, localization, and sex classification of faces from arbitrary viewpoints and under occlusion. Trans. Pattern Anal. Mach. Intell., 31(9):1567–1581, 2009.
[25] G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical report, Technical Report 07-49, University of Massachusetts, Amherst, 2007.
[26] Flavio H. de B. Zavan, Nathaly Gasparin, Julio C. Batista, Luan P. e Silva, Vıtor Albiero, Olga R. P. Bellon and Luciano Silva, Face Analysis in the Wild, 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials.
[027] Bartłomiej Hebda and Tomasz Kryjak, A compact deep convolutional neural network architecture for video based age and gender estimation, Proceedings of the Federated Conference on Computer Science and Information Systems 2016 .
[28] Vladimir Khryashchev, Andrey Priorov and Alexander Ganin, Gender and age recognition for video analytics solution, Image Processing Laboratory, P.G. Demidov Yaroslavl State University Yarodlavl, Russia, 2014 IEEE.
[29] Gil Levi and Tal Hassner, Age and Gender Classification using Convolutional Neural Networks,Department of Mathematics and Computer Science The Open University of Israel, 2015 IEEE [30] Vladimir Khryashchev, Lev Shmaglit, Andrey Shemyakov, Anton Lebedev Yaroslavl State University, Gender Classification for Real-Time Audience Analysis System, PROCEEDING OF THE 15TH CONFERENCE OF FRUCT ASSOCIATION 2015, Russia.
[31] Lijia Lu, Weiyang Liu, Yandong Wen, and Yuexian Zou, Automatical Gender Detection for Unconstrained Video Sequences based on Collaborative Representation, ICSP2014 Proceedings.
[32] Ke Zhang, Ce Gao, Liru Guo, Miao Sun, Xingfang Yuan, Tony X. Han, Zhenbing Zhao and Baogang Li, Age Group and Gender Estimation in the Wild with Deep RoR Architecture, Age Group and Gender Estimation in the Wild with Deep RoR Architecture, IEEE TRANSACTIONS ON L ATEX CLASS FILES, VOL. XX, NO. X, AUGUST 2017.
[33] Meltem Demirkus, Matthew Toews, James J. Clark, Combining Motion and Appearance for Gender Classification from Video Sequences, IEEE 2016.
[34] Hu Han, Member, Anil K. Jain, Shiguang Shan and Xilin Chen, Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach, IEEE Transactions on Pattern Analysis and Machine Intelligence.