A review on identification of gender using fingerprints
Keywords:
fingerprints, fingerprints impression, biometrics, machine learning, data mining, ridge density, SVM, neural networkAbstract
Every person in the world has unique biometrics characteristics such as iris, face, voice, palm or finger-vein patterns, and fingerprints. Biometrics, such as fingerprints are even more distinctive than DNA. Although identical twins can share DNA, they cannot have identical fingerprints. The fingerprint impressions are created by using ridges and valleys which are present on the surface of fingers. Fingerprints help to afford an infallible means of personal identification because the ridge arrangement on each person's finger is unique and does not change with growth or age. Some studies in machine learning and data mining investigate a relationship between fingerprint and gender. Hereby using ridges present on the finger it can be identified that fingerprint is of male or female, as males have larger body size than females, the equal number of ridges on a larger surface area means males have a lower fingerprint ridge density; finding out the gender from fingerprints can reduce the search space to half. The criminal justice system uses fingerprints to authenticate a convicted offender's identification and track their previous arrests and convictions, criminal tendencies, known associates, and other important information in the absence of DNA.
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