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Estimation of Age Through Fingerprints Using Wavelet Transform and Singular Value Decomposition
Gnanasivam P, Dr. S. Muttan
Pages - 58 - 67     |    Revised - 15-03-2012     |    Published - 16-04-2012
Volume - 6   Issue - 2    |    Publication Date - April 2012  Table of Contents
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KEYWORDS
Age Estimation, Discrete Wavelet Transform, Singular Value Decomposition, K-nearest Neighbor
ABSTRACT
The forensic investigators always search for fingerprint evidence which is seen as one of the best types of physical evidence linking a suspect to the crime. In this paper discrete wavelet transform (DWT) and the singular value decomposition (SVD) has been used to estimate a person’s age using his/her fingerprint. The most robust K nearest neighbor (KNN) used as a classifier. The evaluation of the system is carried on using internal database of 3570 fingerprints in which 1980 were male fingerprints and 1590 were female fingerprints. Tested fingerprint is grouped into any one of the following five groups: up to 12, 13-19, 20-25, 26-35 and 36 and above. By the proposed method, fingerprints were classified accurately by 96.67%, 71.75%, 86.26%, 76.39% and 53.14% in five groups respectively for male and similarly classified by 66.67%, 63.64%, 76.77%, 72.41% and 16.79% in five groups respectively for female.
CITED BY (17)  
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6 Tarare, S., Anjikar, A., & Turkar, H. (2015, February). Fingerprint Based Gender Classification Using DWT Transform. In Computing Communication Control and Automation (ICCUBEA), 2015 International Conference on (pp. 689-693). IEEE.
7 Agrawal, H., & Choubey, S. Fingerprint Based Gender Classification using multi-class SVM.
8 Dabi, D. S., & Patil, S. B. A Robust Age Estimation System For Indian Facial Image using 2D-Gabor Filter and Multilinear Principle Component Analysis.
9 Shinde, M. K., & Annadate, S. A. Study of different methods for Gender Identification using Fingerprints.
10 Ceyhan, E. B., Sagroglu, S., Tatoglu, S., & Atagun, E. (2014, December). Age Estimation from Fingerprints: Examination of the Population in Turkey. In Machine Learning and Applications (ICMLA), 2014 13th International Conference on (pp. 478-481). IEEE.
11 Mason, S., Gashi, I., Lugini, L., Marasco, E., & Cukic, B. (2014, June). Interoperability between fingerprint biometric systems: An empirical study. In Dependable Systems and Networks (DSN), 2014 44th Annual IEEE/IFIP International Conference on (pp. 586-597). IEEE.
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13 Akbar, S., Ahmad, A., & Hayat, M. (2014). Identification of Fingerprint Using Discrete Wavelet Transform in Conjunction with Support Vector Machine.
14 Merkel, R. (2014). New solutions for an old challenge: chances and limitations of optical, non-invasive acquisition and digital processing techniques for the age estimation of latent fingerprints. Logos Verlag Berlin GmbH.
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Associate Professor Gnanasivam P
Agni College of Technology - India
pgnanasivam@yahoo.com
Professor Dr. S. Muttan
Anna University - India