Age and Gender Determination from Finger Prints using RVA and dct Coefficients

Abstract

-In the presented work, age and gender of a person from finger print impression has been worked out. The novelty in the solution lies in the fact that the identification of age and sex is independent from the pressure i.e. finger prints thickness or ridge/valley thickness. The age and gender finger prints are classified on the basis of ridge to valley area, entropy and rms value of dct coefficients. The classification is described in the result section Keywords: -DPI  Dots per Inch, RVA  Ridge to valley Area, RMS  Root Mean Square I. INTRODUCTION Sex identification of suspect from crime scene is an important task in forensic science that minimizes the search population of suspects. Existing methods for gender classification have limited use for crime scene investigation because they depend on the availability of teeth, bones, or other identifiable body parts having physical features that allow gender and age estimation by conventional methods. Various methodologies has been used to identify the gender using different biometrics traits such as face, gait, iris, hand shape, speech and fingerprint. Fingerprint has been used as a biometric for the gender and age identification because of its unique nature and do not change throughout the life of an individual. Wavelet transform is a popular tool in image processing and computer vision because of its complete theoretical framework, the great flexibility for choosing bases and the low computational complexity. As wavelet features has been popularized by the research community for wide range of applications including fingerprint recognition, face recognition and gender identification using face, authors have confirmed the efficiency of the DWT approach for the gender identification using fingerprint. The SVD approach is selected for the gender discrimination because of its good information packing characteristics and potential strengths in demonstrating results. The SVD method is considered as an information oriented technique since it uses principal components analysis procedures (PCA), a form of factor analysis, to concentrate information before examining the primary analytic issues of interest. K-nearest neighbors (KNN), gives very strong consistent results.