IOSR Journal of Engineering (IOSRJEN) e-ISSN: 2250-3021, p-ISSN: 2278-8719 Vol. 3, Issue 8 (August. 2013), ||V5|| PP 05-09 www.iosrjen.org 5 | P a g e Age and Gender Determination from Finger Prints using RVA and dct Coefficients Ravi Wadhwa, Maninder Kaur, Dr. K.V.P. Singh 1 Doaba Institute of Eng. and Tech., Kharar, Mohali 2 DIET, Kharar, Mohali 3 DIET, Kharar, Mohali 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. II. RELATED WORKS A novel method of gender Classification from fingerprint is proposed based on discrete wavelet transform (DWT) and singular value decomposition (SVD). The classification is achieved by extracting the energy computed from all the sub-bands of DWT combined with the spatial features of non-zero singular values obtained from the SVD of fingerprint images. K nearest neighbor (KNN) used as a classifier. This method is experimented with the internal database of 3570 fingerprints finger prints in which 1980 were male fingerprints and 1590 were female fingerprints. Finger wise gender classification is achieved which is 94.32% for the left hand little fingers of female persons and 95.46% for the left hand index finger of male persons. Gender classification for any finger of male persons tested is attained as 91.67% and 84.69% for female persons respectively. Overall classification rate is 88.28% has been achieved [1]. This study evaluated fingerprint quality across two populations, elderly and young, in order to assess age and moisture as potential factors affecting utility image quality. Specifically, the examination of these variables was conducted on a population over the age of 62, and a population between the ages of 18 and 25, using two fingerprint recognition devices (capacitance and optical). Collected individual variables included: age, gender, ethnic background, handedness, moisture content of each index finger, occupation(s), subject's use of hand moisturizer, and prior usage of fingerprint devices. Computed performance measures included failure to enroll, and quality scores. The results indicated there was statistically significant evidence that both age and moisture affected effectiveness image quality of each index finger at a=0.01 on the optical device, and there was statistically significant evidence that age affected effectiveness image quality of each index finger on the capacitance device, but moisture was only significant for the right index finger at a=0.01 [2].