Advances in Fingerprint Technology.pdf

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Fingerprint Quality Acquisition Estimation Fingerprint Indexing Feature Extraction Fingerprint Matching Match Verification Fingerprint Enhancement Feature Editing Figure 8.2 Functional block diagram of an “automatic” fingerprint identification system. The dotted lines illustrate alternative paths. Some of the functional blocks (e.g., indexing) can be performed either by an expert or a computer. The feature editing and match verification tasks are performed by an expert. Typically, a fingerprint matcher passes a ranked list of 10 to 100 fingerprints for the match verification stage; a fingerprint expert browses the original fingerprint images (as opposed to their representations) to confirm/reject a candidate match. biometric measurements. Second, people are concerned about linkages: unauthorized usage of biometric measurements across different identification systems (e.g., criminal and civilian fingerprint identification systems) to link the identities of people and to gather/track individual information that may otherwise be unavailable. It is necessary to enforce systemwide mechanisms to ensure the usage of the biometric measurement for its proscribed intent. As novel applications of fingerprints (and other biometric identifiers) become more widespread, law enforcement agencies will be increasingly involved in resolving the frauds involving repudiation (e.g., users denying having accessed the system), coercion (e.g., users claiming to have been forced into the system), contamination (e.g., erroneous acquisition of biometrics identifier not associated with the intended user), and circumvention (e.g., unauthorized user
illegitimately gaining access to the system). Consequently, agencies may not only be required to pass judgments about the identities related to biometric identifiers but also about the integrity of the systems and the validity of the biometric measurements. System Architecture A fingerprint identification system is an automatic pattern recognition system that consists of three fundamental stages: (1) data acquisition: the fingerprint to be recognized is sensed; (2) feature extraction: a machine representation (pattern) is extracted from the sensed image; and (3) decision-making: the representations derived from the sensed image are compared with a representation stored in the system. The comparison typically yields a matching score quantifying the similarity between the two representations. If the score is higher than a threshold (determined by the system operating point [see Figure 8.1]), the representations are determined to have originated from the same finger(s). In an identification system, multiple comparisons may be needed. Often, the stored representations in the database are partitioned into bins, based either on information extrinsic to the sensed input measurements (e.g., sex and age of the individual) or on information intrinsic to the sensed image (e.g., fingerprint class or type [see “Fingerprint Classification” section]). As a result, the input fingerprint need not be searched in the entire database, but only in the particular bin(s) of interest. Different systems may use different numbers of available fingerprints (multiple impressions of a single finger or single impressions of multiple fingers) for person identification. The feature extraction stage may involve manual override and editing by experts. Image enhancement may be used for poor-quality images (see “Fingerprint Enhancement” section). Challenges While significant progress has been made in automatic fingerprint identification, there are still a number of research issues that need to be addressed to improve system accuracy. Most of the shortcomings in the accuracy of an automatic fingerprint identification system can be attributed to the acquisition process: Inconsistent Contact The act of sensing distorts the fingerprint. Determined by the pressure and contact of the finger on the glass platen, the three-dimensional shape of the
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Advances in Advances Fingerprint Te
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Advances in Fingerprint Technology
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Preface The first edition of this b
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Acknowledgments We gratefully ackno
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Table of Contents Preface Acknowled
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History and Development of Fingerpr
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Figure 1.2 Basic fingerprint patter
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Figure 1.3 Portion of the prehensil
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Figure 1.4 Elliptical whorl. Theory
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The bricks, carefully laid and accu
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the megalithic builders, including
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made most of them. These “identif
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Figure 1.6 Nehemiah Grew. (Drawn by
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Figure 1.7 Right palm imprint in pl
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By kindly words of persuasion a ref
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Loop: Now if this oblique stripe by
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Figure 1.11 Dr. Ivan Vucetich. (Dra
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Sir Edward Henry and Sir William He
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in charge he undoubtedly supported
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1. Finding finger imprints on prehi
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Faulds edited seven issues of a fin
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much of his inspired research. For
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to the unrelenting efforts of Steeg
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and gathered around him a nucleus o
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I find that portions of palm imprin
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Identification of Latent Prints ROB
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Without it, no amount of further la
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work will quite often fare badly un
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Figure 2.3 Polygon method. Overlay
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Experience and Skill Although the t
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The initial identification of the l
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D5 D6 D7 I2 D8 D9 D10 No No No Reco
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D11 I4 No D12 D11 I5 D12-1 D7 D12-2
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D14 I7 D16 C3 D11 D14-2 D7 D14-3 C2
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Figure 2.7 Example of pressure dist
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9. Mairs, G., Novel method of print
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DNA From Latent Prints DNA From Blo
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Figure 3.1 A schematic diagram show
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Figure 3.3 A schematic diagram of t
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(5 to 12 µg/L), bromide (0.2 to 0.
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electrophoresis (SDS-PAGE) include
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Table 3.3 Anatomical Variation in t
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Various oxidative and bacteriologic
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acid content can change with time i
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are of sebaceous origin. 82 Approxi
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Table 3.7 Changes in Surface Lipid
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gland’s activity. The more active
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content. 108 Palmitic acid was foun
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Figure 3.4a A chromatogram of a fin
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various lipid classes found in a la
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of DNA using RFLP. 130 No adverse e
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was later found to be caused by the
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17. Seutter, E., Goedhart-De Groot,
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52. Downing, D. T., Strauss, J. S.,
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85. Summerly, R., Yardley, H. J., R
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120. Duff, J. M. and Menzel, E. R.,
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149. Kloosterman, A., Application o
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Coumarin 540 Dye Staining Method An
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visualization of latent fingerprint
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White powder Dolomite, starch powde
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Fluorescein solution (in methanol/w
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Notes: Dissolve the MoS 2 in the di
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4. Avoid inhaling any iodine fumes
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CN CN CN A - A - CH2 C COOR CH2 C C
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Large vacuum chambers for processin
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Gentian Violet Solution — Non-Phe
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Table 4.1 Approximate Absorption an
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and recorded by autoradiography. Ni
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Ninhydrin solutions may be applied
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of development of ninhydrin-treated
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and certain nonreactive surface mat
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Procedure Xylene 50 mL Petroleum et
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ehavior of Ruhemann’s purple-meta
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4. Rinse the specimen in tap water.
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een treated with ninhydrin. In addi
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Working solution Stock solution 6 m
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Note: Combine and stir until citric
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Zauner 181 noted that on a rare occ
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3. Expose the tape surface to a hig
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lifting media were used, followed b
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under certain circumstances. The Br
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Physical Methods Visual Examination
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DRY Super Glue Fuming Powder Dustin
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Visual Examination Photography Iodi
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References 1. Olsen, R. D., Scott
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36. Springer, E. and Bergman, P., A
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74. McCarthy, M. M., Evaluation of
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110. Mooney, D. G., Development of
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142. Pounds, C. A., Grigg, R., and
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175. Jones, R. J. and Pounds, C. A.
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213. Hebrard, J. and Donche, A., Fi
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245. Misner, A., Wilkinson, D., and
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Fingerprint Development by Ninhydri
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Figure 5.1 2,2-Dihydroxy-1,3-indane
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Figure 5.4 Formation of murexide (V
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Figure 5.6 The 1,3-dipole form of t
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less volatile 1,1,2-trichloroethane
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Key to Routes Primary Special Secon
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Specially designed cabinets for che
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the importance of cooling the objec
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Figure 5.10 Ninhydrin (I) and some
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een prepared and evaluated as finge
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Figure 5.14 The red pigment formed
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Figure 5.16 The fluorescent product
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16. Pounds, C.A. and Jones, R.J., P
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51. Jungbluth, W.O., Replacement fo
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87. Kent, T., An operational guide
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116. Kobus, H.J., Pigou, P.E., Dell
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147. Dayan, S., Almog, J., Khodzhae
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Figure 6.1 Ninhydrin/ZnCl 2 vs. nin
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purposes of understanding their pho
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Ar-laser image monitor lens light c
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laser Figure 6.6 Block diagram of p
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step 1 step 2 step 3 fingerprint co
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nanocrystals. Photoluminescent semi
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Figure 6.11 Photoluminescence of fi
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H H H H N N CH2CH2 CH2CH2 H N N H N
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sample, rather than preferential ad
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Page 242 and 243: The amidation reaction depicted in
Page 244 and 245: Figure 6.20 Photoluminescence of fi
Page 246 and 247: Figure 6.22 Photoluminescence of fi
Page 248 and 249: R 1 - COOH + HO - N R 1 O C N H Fig
Page 250 and 251: 29. Sooklal, K., Hanus, L.H., Ploeh
Page 252 and 253: Procedure Water and Acid Pretreatme
Page 254 and 255: The Silver Physical Development Pro
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Page 258 and 259: ions (found only on paper that has
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Page 264 and 265: Saunders. 22 Both workers recognize
Page 266 and 267: Figure 7.2 Scanning electron micros
Page 268 and 269: paper and these convert to ferric o
Page 270 and 271: Water and Acid Pretreatments The wa
Page 272 and 273: Figure 7.4 Comparison of a ninhydri
Page 274 and 275: Figure 7.7 Comparison of a ninhydri
Page 276 and 277: (0.2%) to not form silver oxide whe
Page 278 and 279: X-Ray and Scanning Electron Microsc
Page 280 and 281: is much to be done to optimize the
Page 282 and 283: 3. Margot, P. and Lennard, C., Fing
Page 284 and 285: 35. Morris, J.R. and Wells, J.M., A
Page 286 and 287: Introduction More than a century ha
Page 288 and 289: False Reject Rate (FRR) Civilian Ap
Page 292 and 293: finger gets mapped onto the two-dim
Page 294 and 295: (a) (b) (c) Figure 8.3 Fingerprint
Page 296 and 297: In forensics, a special kind of ink
Page 298 and 299: L A B (a) Finger Friction Surface R
Page 300 and 301: Fingerprint Representation A finger
Page 302 and 303: (a) (b) (c) (d) Figure 8.8 A finger
Page 304 and 305: Figure 8.10 Relative configuration
Page 306 and 307: A minutiae feature extractor finds
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Page 310 and 311: 1. Singular points. The Poincare in
Page 312 and 313: Table 8.2 shows the results of the
Page 314 and 315: (a) (b) Figure 8.13 Two different i
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Page 318 and 319: (a) (b) Figure 8.16 Fingerprinting
Page 320 and 321: 0.5 -0.5 -1 200 1 0.8 0.6 0.4 0.2 1
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Page 324 and 325: (a) (b) Figure 8.19 Examples of enh
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Page 328 and 329: or otherwise. Therefore, there are
Page 330 and 331: Conclusions and Future Prospects Fi
Page 332 and 333: WSQ-related illustrations (Figure 8
Page 334 and 335: 37. Siemens. The ID Mouse from Siem
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Page 338 and 339: Trauring Model (1963) Description o
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correspondence in friction ridge de
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extreme variability of friction rid
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5. Trauring 19 6. Kingston 20 7. Os
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variation need have no relationship
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1. Fork directed to the right 2. Fo
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e closer to reality. In any case, t
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There is not only opportunity for c
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that, in practice, it is relative d
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e the probability that there will b
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Correction factors (G) were introdu
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fingerprint individuality into two
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appreciated that the number of tria
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a corresponding reference point is
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Table 9.1 Kingston’s Relative Fre
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minutia counts in different-sized r
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If one is to use the compound forms
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Osterburg Model (1977-1980) Descrip
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of any occurrences that appear in t
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minutiae. Both the Kingston and Ost
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minutia orientation results in regi
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Orientation for minutiae was define
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the allowable combinations of minut
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selected for the study, based on th
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Table 9.4 Champod’s Upper Bound F
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that Champod and Margot proved to b
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fingerprint was isolated. This area
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that, for the experiments as design
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can be encouraged by the dedication
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37. Amy, L., Valeur de la preuve en
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The Expert Fingerprint Witness ROBE
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Qualifications of the Fingerprint E
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7. Results of comparisons conducted
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granted for a pretrial conference,
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any examination that I have conduct
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to fully understand the meaning of
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When seated in the witness stand, t
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jury but looked down at the floor i
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Courtroom Courtesy When in court, i
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If the attorney does not have the p
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Glossary of Commonly Used Courtroom
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Indictment An accusation in writing
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APPENDIX Daubert Hearings EDWARD GE
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The U.S. Government set presented e
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Presided over by the Honorable J. C
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