Abstract

Gender classification is the major and challenging task in the field of forensic anthropology which minimizes the list of suspects search. The existing systems use the availability of bones, teeth and other identifiable body parts having physical features that allow gender and age estimation by conventional methods. The different biometrics traits such as face, gait, iris, speech and fingerprint are used to identify the gender and age. Among the biometrics, fingerprint is most commonly available in any crime scene. In this study, an efficient algorithm to identify the gender of a given fingerprint into male or female is proposed. The two most efficient techniques are utilized to enhance the performance of the gender classification system. As the first step, Un-decimated Wavelet Transform (UWT) is employed to extract the features from the fingerprints by applying ranking. Secondly, Gaussian Mixture Models (GMMs) technique is used as classifier for the process of gender classification. The proposed system is carried out with the database of 180 persons finger prints of all fingers in which 80 are female and 100 are male. The results show the satisfactory classification accuracy of over 90%.

Keywords:

Gaussian mixture model, gender classification, stationary wavelet transform, wavelet transform,

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Competing interests

The authors have no competing interests.

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