Fingerprint Based Gender Classification Using 2D Discrete Wavelet Transforms and Principal Component Analysis

Fingerprint evidence is undoubtedly the most reliable and acceptable evidence till date in the court of law. Fingerprints are obtained at the site of crime and in many old monuments and in excavated things. Estimating the gender of fingerprints is an emerging field and many methods using the fingerprint physical features like the ridge count and the ridge thickness have been used so far. 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 using frequency domain technique and a pattern recognition technique. The combined processing has provided better results. This paper aims in using 2D-Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) combined to classify gender using an obtained fingerprint. The minimum distance method was used as a classifier. Fingerprints of 200 males and 200 females belonging to the various age groups were taken for analysis. The experimental results show good for trained database. It was found that increasing the database population in each category improves the performance of the system.