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fingerprints age estimation using DWT and SVD
« on: January 15, 2017, 12:00:00 AM »
fingerprints  age estimation using DWT and SVD

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INTRODUCTION
 
  Age information is important to provide investigative leads for finding unknown persons. Existing methods for age estimation 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 age estimation by conventional methods. In this paper, age of a person is estimated from the fingerprints using DWT and SVD. The science of fingerprint has been used generally for the identification or verification of person and for official documentation. Fingerprint analysis plays a role in convicting the person responsible for an audacious crime. 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.
  In fingerprint, the primary dermal ridges (ridge counts) are formed during the gestational weeks 12-19 and the resulting fingerprint ridge configuration is fixed permanently. Ridges and their patterns exhibit number of properties that reflect the biology of individuals. Fingerprints are static and its and shape changes may vary with age but basic pattern of the fingerprint remains unchanged. Also, the variability of epidermal ridge breadth in humans is substantial. Dermatoglyphic features statistically differ between the sexes, ethnic groups and age categories. Gender and age determination of unknown can guide investigators to the correct identity among the number of possible matches. The fingerprint age determination is a source of information regarding the study of morphological, physical, chemical and biochemical transformations and it provides important material for the relational interpretative terms between the traces existing at the crime scene, the temporal space and the group of individuals.
  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. Here  discrete wavelet transform  (DWT)  and  the  singular  value  decomposition  (SVD)  has  been  used  to  estimate  a persons age using his/her fingerprint. The most robust K nearest neighbor (KNN) used as a classifier. Age  information  is  important  to  provide  investigative  leads  for  finding  unknown  persons.
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 result.

 LITERATURE SURVEY

Many believe that ancient civilizations were the first to use fingerprinting. Ancient Babylonians pressed their fingerprints into clay tablets to conduct business transactions. The Persians were also known to use fingerprints on official documents. In 1882, Gilbert Thomson of the U.S. Geological Survey used his own fingerprint on a document the first known use of fingerprinting in the United States. Eventually, governments throughout the Western world began employing fingerprints for criminal identification. In 1903, the New York State Prison system began systematic fingerprinting of criminals. In the same year, the U.S. Army also began fingerprinting enlisted men for identification purposes.
 

 A Normal Fingerprint

At first, fingerprints had to be examined for a match manually. Matches had to agree in 12 different points in order to be considered valid. This was a painstaking and largely subjective process that was often more tedious than it was helpful. However, in 1980, the Federal Bureau of Investigation (FBI) created a computerized Criminal Fingerprint File. The FBI also began regulating the methods of gathering and classifying fingerprints, and created a searchable database which made finding fingerprint matches much easier. Since then, fingerprinting has become a common form of evidence collection and has proved a vital clue in many criminal cases. Figure 2.1 shows the picture of a normal finger print
The increasing use of automated fingerprint recognition puts on it a challenge of processing a diverse range of fingerprints. The quality control module is important to this process because it supports consistent fingerprint detail extraction which helps in identification / verification. Inherent feature issues, such as poor ridge flow, and interaction issues, such as inconsistent finger placement, have an impact on captured fingerprint quality, which eventually affects overall system performance. Aging results in loss of collagen; compared to younger skin, aging skin is loose and dry. Decreased skin firmness directly affects the quality of fingerprints acquired by sensors. Medical conditions such as arthritis may affect the user’s ability to interact with the sensor, further reducing fingerprint quality. Because quality of fingerprints varies according to the user population’s ages and fingerprint quality has an impact on overall system performance, it is important to understand the significance of fingerprint samples from different age groups.
Identity is a set of physical characteristics, functional or psychic, normal or pathological that defines an individual. Recently, there has been an increased interest in biometric technologies that is human identification based on one's individual features. The various identification data used are fingerprints, handwriting, bite marks, DNA fingerprinting etc. Fingerprints are constant and individualistic and form the most reliable criteria for identification. A fingerprint is an impression of the friction ridges of all part of the finger. A friction ridge is a raised portion of the epidermis on the digits or on the palmar and plantar skin, consisting of one or more connected ridge units of friction ridge skin.
Another approach to find age of fingerprints is to establish an estimation relationship of the age of fingerprints left on surfaces, by morphological, structural, macro and microscopic examinations, together with biochemical and titration DNA tests in order to confirm the rate of biological degradation during a certain period. The capacity of counting the age of a fingerprint lead to the possibility to place it in time and to correlate it with the time of doing the criminal act, bringing us information about the presence of a person in a certain place and period. As research methods, forensic techniques for fingerprints, as well as cytology and molecular biological methods (DNA analysis, DNA quantification with TaqMan using Real Time PCR)is used. The estimation of the age of fingerprints using these methods offers us the advantages of standardization based on relationships between morphological and biochemical characteristics depending on time, as well as the possibility to assign as a rough guide a blood type to an individual
Only few works were concentrated in the age estimation using the fingerprint. There are no age verification biometrics, no age determination biometrics and no age estimation biometrics based on the fingerprint ridge width the age and sex were determined. Many Earlier works were related the fingerprint image quality and the age of a person. Other works in the way of age estimation used the speech recognition, face etc. Here a method of identifying range of the age using the discrete wavelet transform and the singular value decomposition is explained.

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fingerprints age estimation using DWT and SVD
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