Fingerprint evidence is undoubtedly the most reliable and acceptable evidence till date in the court of law. Due to the immense potential of fingerprints as an effective method of identification an attempt has been made in the present work to analyze their correlation with gender of an individual. This prospective study was carried out over a period of 2 months among 500 public people(250 male & 250 female) belonging to the various age groups between 1 - 90. Features extracted were; ridge count, ridge thickness to valley thickness ratio (RTVTR), white lines count, and ridge count asymmetry, and pattern type concordance. For gender classification Support Vector Machines (SVM) was used for the classification using the most dominant features. Results are calculated by our proposed method. This analysis makes the proposed method better accurate than existing methods.

1. Pillay, V. V. Textbook of Forensic Medicine and Toxicology. 15th ed. Hyderabad: Paras Medical Publishers, 2009: 53-94.
2. Vij, K. Textbook of Forensic Medicine and Toxicology. 3rd ed. New Delhi: Elsevier, 2005: 89-91.
3. Sharat Chikkerur, Venu Govindaraju, and Alexander N. Cartwright, "Fingerprint Image Enhancement Using STFT Analysis," In Proc. of the Third International Conference on Advances in Pattern Recognition, ICAPR 2005, Bath, UK, pp. 20-29, 2005.
4. Gholamreza Amayeh, George Bebis and Mircea Nicolescu, "Gender Classification from Hand Shape," In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW '08), Anchorage, AK, pp. 1-7, Jun 2008.
5. Kathryn A Kamp, Nichole Timmerman, Gregg Lind, Jules Graybill and Ian Natowsky, "Discovering Childhood: Using Fingerprintd to Find Children in the Archaeological Record," Journal of Ancient Fingerprints, Vol. 64, No. 2, pp. 309-315, 1999.
6. Hui-cheng Lian and Bao-liang Lu, "Multi-view Gender Classification Using Local Binary Patterns and Support Vector Machines," In Proc. of the International Symposium on Neural Networks, Chengdu, China, pp. 202–209, 2006.
7. Ahmed Badawi, Mohamed Mahfouz, Rimon Tadross, and Richard Jantz, "Fingerprint-Based Gender Classification," In Proc. of the 2006 International Conference on Image Processing, Computer Vision, Pattern Recognition, Las Vegas, Nevada, USA, Vol. 1, Jun 2006.
8. G. Ramaswamy, Vuda Sreenivasarao, P. Ramesh, and D. Ravi Kiran, "A Novel Approach for Human Identification through Fingerprints," International Journal of Computer Applications, Vol. 4, No. 3, pp. 35-42, Jul 2010.
9. P. Gnanasivam and S. Muttan, "Gender Identification Using Fingerprint through Frequency Domain Analysis," European Journal of Scientific Research, Vol. 59 No. 2, pp. 191-199, 2011.
10. Miroslav Kralik and Vladimir Novotny, "Epidermal Ridge Breadth: An Indicator of Age and Sex in Paleodermatoglyphics," Journel of Variability and Evolution, Vol. 11, pp. 5–30, 2003.
11. Ravi. J, K. B. Raja and Venugopal. K, "Fingerprint Recognition Using Minutia Score Matching," International Journal of Engineering Science and Technology, Vol. 1, No. 2, pp. 35-42, 2009.

Gender Classification Finger Print Support Vector Machines (svm) Minutiae Extraction