Gender Classification based on Fingerprints using SVM

Romany F. Mansour, Abdulsamad Al-Marghilnai, Meshrif Alruily

Abstract

The fingerprint is commonly used biometric method for person identification. It is the most conventional and widely used technique in forensics and criminalities. Identification of the person's age and gender based on his/her fingerprint is an important step in overall person's identification. The aim of this research paper is to propose a gender classification technique based on fingerprint characteristics of individuals using discrete cosine transform (DCT). Gender classification evaluated using dimensionality reduction techniques such as Principal Component Analysis (PCA), along with Support Vector Machine (SVM). A dataset of 2600 persons of different ages and sex was collected as internal database. Of the samples tested, 1250 samples of 1375 exactly identified male samples and 1085 samples of 1225 exactly identified female samples.

References

  1. Acree, Mark A.,1999, "Is there a gender difference in fingerprint ridge density?." Forensic science international 102.1, 35-44.
  2. Austin R., Christopher L., 2001, “Implications of the IDENT/IAFIS Image Quality Study for Visa Fingerprint Processing”, Mitertek Systems (MTS).
  3. Badawi A., Mahfouz M., Tadross R., and Jantz R., 2006, "Fingerprint-Based Gender Classification," In Proc. of the International Conference on Image Processing, Computer Vision, Pattern Recognition, Las Vegas, Nevada, USA, Vol. 1.
  4. Belhumeur V., Hespanha J., and Kriegman D., 1997,” Eigenfaces vs.fisherfaces: Recognition using class specific linear projection”, Transactions on Pattern Analysis and Machine Intelligence, PAMI19 (7): 711- 720.
  5. Girgis M., Sewisy A., Mansour R., 2009, "A robust method for partial deformed fingerprints verification using genetic algorithm", Expert Systems with Applications 36, PP. 2008-2016.
  6. Gnanasivam .P, and Dr. Muttan S,2012, “Gender Identification Using Fingerprint through Frequency Domain analysis” IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 3.
  7. Gungadin S. 2007, “Sex Determination from Fingerprint Ridge Density”. Internet Journal of Medical;2(2):4-7.
  8. Hall J. and Kimura D.1994, Dermatoglyphic asymmetry and Sexual Orientation in Men. Behavioral Neuroscience, 108, 1203-1206.
  9. IBG, 2007. Biometrics Market and Industry Report. IBG: NY.
  10. Jayadevan R., Jayant Kulkarni V., Suresh N. Mali,Hemant K.,2006,“A New Ridge Orientation based method for feature extraction from fingerprint images, ” Proceedings of World Academy of Science, Engineering and Technology, Volume 13.
  11. Karine C., Christopher M. and Martin L.2000, “Birth Order, Birth Interval, and Deviant Sexual Preferences among Sex Offenders.” Sexual Abuse: A Journal of Research and Treatment, Vol. 4, No. 1.
  12. Kirby M., Sirovich L., 1990,"Application of the Karhunen-Loeve procedure for the characterization of human faces” IEEE Trans. Pattern. Anal Machine Intelligence, vol. 12, no. 1, pp. 103-108.
  13. Kralik, M., Novotny V. 2003,” Epidermal ridge breadth: an indicator of age and sex in paleodermatoglyphics”, Variability and Evolution, Vol. 11: 5-30.
  14. Moghaddam, B.; Yang M., 2000, ”Gender classification with support vector machines. In: Automatic Face and Gesture Recognition”, 2000. Proceedings. Fourth IEEE International Conference on. IEEE, p. 306-311.
  15. Rijo J., Arulkumaran T., 2013 “Fingerprint Based Gender Classification Using 2D Discrete Wavelet Transforms and Principal Component Analysis”. International Journal of Engineering Trends and Technology, Volume 4 Issue 2.
  16. Ritu K. and Susmita G., 2012, “Fingerprint Based Gender Identification using Frequency Domain Analysis”. International Journal of Advances in Engineering & Technology.
  17. Sanders G., Kadam A. 2001, “Prepubescent children show the adult relationship between dermatoglyphic asymmetry and performance on sexually dimorphic tasks.”, Cortex., 37(1):91-100.
Download


Paper Citation


in Harvard Style

F. Mansour R., Al-Marghilnai A. and Alruily M. (2014). Gender Classification based on Fingerprints using SVM . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 241-244. DOI: 10.5220/0004721602410244


in Bibtex Style

@conference{icaart14,
author={Romany F. Mansour and Abdulsamad Al-Marghilnai and Meshrif Alruily},
title={Gender Classification based on Fingerprints using SVM},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={241-244},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004721602410244},
isbn={978-989-758-015-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Gender Classification based on Fingerprints using SVM
SN - 978-989-758-015-4
AU - F. Mansour R.
AU - Al-Marghilnai A.
AU - Alruily M.
PY - 2014
SP - 241
EP - 244
DO - 10.5220/0004721602410244