CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
Reseach Article

Transgender Face Recognition using ROI based Convolutional Neural Network

by R. Bhuvaneswari, S. Ganesh Vaidyanathan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 8
Year of Publication: 2021
Authors: R. Bhuvaneswari, S. Ganesh Vaidyanathan
10.5120/ijca2021921360

R. Bhuvaneswari, S. Ganesh Vaidyanathan . Transgender Face Recognition using ROI based Convolutional Neural Network. International Journal of Computer Applications. 183, 8 ( Jun 2021), 1-4. DOI=10.5120/ijca2021921360

@article{ 10.5120/ijca2021921360,
author = { R. Bhuvaneswari, S. Ganesh Vaidyanathan },
title = { Transgender Face Recognition using ROI based Convolutional Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2021 },
volume = { 183 },
number = { 8 },
month = { Jun },
year = { 2021 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number8/31944-2021921360/ },
doi = { 10.5120/ijca2021921360 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:16:11.399532+05:30
%A R. Bhuvaneswari
%A S. Ganesh Vaidyanathan
%T Transgender Face Recognition using ROI based Convolutional Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 8
%P 1-4
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This framework deals with a distinct problem domain where face recognition is performed for individuals who have undergone gender transformation over a period of time. The recognition rate of face recognition stands a major challenge in dealing with the pictures or video frames of transgender individuals. Typically the sexual orientation change causes serious modifications in the actual appearance of the face just as in the body of a transgender individual. Therefore, it presents extra complexity / burden in taking care of the accuracy as far as transsexual face acknowledgement. Subsequently, there is a requirement for face recognition framework to reliably distinguish the people after they go through sex change. As Convolutional Neural Network (CNN) has demonstrated to be one of the powerful tool in dealing with feature extraction in images, a new framework is presented which uses CNN to increase the recognition rate in transgender images. The proposed model extracts the features of transgender’s face components such as two eyes, nose and mouth using CNN. The CNN have been utilized in the proposed model. The investigations were done on HRT transsexual database.

References
  1. Antitza Dantcheva, Cunjian Chen, and Arun Ross. Can facial cosmetics affect the matching accuracy of face recognition systems? In 2012 IEEE Fifth international conference on biometrics: theory, applications and systems (BTAS), pages 391–398. IEEE, 2012.
  2. Vijay Kumar, Ramachandra Raghavendra, Anoop Namboodiri, and Christoph Busch. Robust transgender face recognition: Approach based on appearance and therapy factors. In 2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), pages 1–7. IEEE, 2016.
  3. Haibin Ling, Stefano Soatto, Narayanan Ramanathan, and David W Jacobs. Face verification across age progression using discriminative methods. IEEE Transactions on Information Forensics and security, 5(1):82–91, 2009.
  4. Gayathri Mahalingam and Chandra Kambhamettu. Face verification of age separated images under the influence of internal and external factors. Image and Vision Computing, 30(12):1052–1061, 2012.
  5. Gayathri Mahalingam, Karl Ricanek, and A Midori Albert. Investigating the periocular-based face recognition across gender transformation. IEEE Transactions on Information Forensics and Security, 9(12):2180–2192, 2014.
  6. Raghavendra Ramachandra, Kiran Raja, Sushma Venkatesh, and Christoph Busch. Collaborative representation of statistically independent filters’ response: An application to face recognition under illicit drug abuse alterations. In Scandinavian Conference on Image Analysis, pages 448–458. Springer, 2017.
  7. Narayanan Ramanathan and Rama Chellappa. Face verification across age progression. IEEE transactions on image processing, 15(11):3349–3361, 2006.
  8. Karl Ricanek and Tamirat Tesafaye. Morph: A longitudinal image database of normal adult age-progression. In 7th International Conference on Automatic Face and Gesture Recognition (FGR06), pages 341–345. IEEE, 2006.
  9. P-G Sator, JB Schmidt, MO Sator, JC Huber, and H H¨onigsmann. The influence of hormone replacement therapy on skin ageing: a pilot study. Maturitas, 39(1):43–55, 2001.
  10. Richa Singh, Mayank Vatsa, Himanshu S Bhatt, Samarth Bharadwaj, Afzel Noore, and Shahin S Nooreyezdan. Plastic surgery: A new dimension to face recognition. IEEE Transactions on Information Forensics and Security, 5(3):441–448, 2010.
  11. Daksha Yadav, Naman Kohli, Prateekshit Pandey, Richa Singh, Mayank Vatsa, and Afzel Noore. Effect of illicit drug abuse on face recognition. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1–7. IEEE, 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Transgender Convolutional Neural Network Support Vector Machine