A Short Review of Gender Classification based on Fingerprint using Wavelet Transform

Abstract

Abstract-In some cases, knowing the gender of fingerprint owner found in criminal or disaster scene is advantageous. Theoretically, if the number of the male and female fingerprints in a database is equal, then the identification process of a fingerprint on that database would be two times faster. Some methods have been used to classify gender based on the fingerprint. Most of the method is based on ridge density. This method showed good result. However, it is sensitive to the location of the fingerprint area where ridge density is determined. This paper reviews some literature that used wavelet transform to generate features of a fingerprint. As far as what we found in literature, the number of papers on this topic is very limited. However, based on the literature we reviewed, wavelet transform gives some advantages compared to ridge density counting.

Forensic Fingerprint Analysis using Self-Organizing Maps, Classification and Regression Trees and Naïve Bayes Methods

“…On the contrary, researchers from the domain of computer science actively explored various machine learning algorithms in discriminating gender based on multiple features of fingerprint [16][17][18][19][20][21][22][23][24][25]. Compared to [11][12][13][14][15] performing manual calculation of number of ridge, [16][17][18][19][20][21][22][23][24][25] tended to extract fingerprint feature assisted by either image processing technique or digitizing the image of fingerprint. For instance, [20] proposed an image processing algorithm to determine the number of ridge.…”