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Estimation of Age Through Fingerprints Using Wavelet Transform and Singular Value Decomposition
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International Journal of Biometrics and Bioinformatics (IJBB)
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Volume:  6    Issue:  2
Pages:  
Publication Date:   April 2012
ISSN (Online): 1985-2347
Pages 
58 - 67
Author(s)  
Gnanasivam P - India
Dr. S. Muttan - India
 
Published Date   
16-04-2012 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
ADDITIONAL INFORMATION
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   Age Estimation, Discrete Wavelet Transform, Singular Value Decomposition, K-nearest Neighbor 
 
 
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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. 
 
 
 
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6 Shannon Brennan and Mia Dauvergne, Police-reported crime statistics in Canada, 2010, http://www.statcan.gc.ca/pub/85-002-x/2011001/article/11523-eng.htm
7 Crime in India 2010-Statistics, National Crime Records Bureau, Ministry of Home Affairs, Government of India, http://ncrb.nic.in.
8 John D. Woodward, Jr., “Is Biometrics an age verification technology”, Testimony, RAND’s publications, June 2000.
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12 Tobias Bocklet, Andreas Maier, Elmar Noth, “Age Determination of Children in Preschool and Primary School Age with GMM-based Super vectors and Support Vector Machines/ Regression”, international conference on Text, Speech and Dialogue, 2008, pp. 1-9,.
13 Mohammad EL Deeb and Motaz El-Saban, “Human age estimation using enhanced bio- Inspired features (EBIF)”, In International Conference on Image Processing (ICIP), 2010, pp. 1589-1592.
14 Khoa Luu, Karl Ricanek, Tien D Bui, and Ching Y Suen, “Age estimation using Active Appearance Models and Support Vector Machine regression”, in International \ Conference on Biometrics Theory Applications and Systems, 2009, pp. 1-5.
15 Andreas Lanitis, Chrisina Draganova, and Chris Christodoulou, “Comparing Different Classifiers for Automatic Age Estimation”, IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, Vol. 34, no.1, pp. 621-628, 2004.
16 Bai-Ling Zhang, Haihong Zhang, and Shuzhi Sam Ge, “Face Recognition by Applying Wavelet Subband Representation and Kernel Associative Memory”, IEEE Transactions on neural networks, vol. 15, no. 1, pp. 166-177, 2004.
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18 Nitgen Company, Fingkey Hamster II fingerprint sensor http://www.nitgen.com/eng/
 
 
 
 
 
 
 
 
Gnanasivam P : Colleagues
Dr. S. Muttan : Colleagues  
 
 
 
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