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presence of a potential threat Police officers monitoring this biometric process stood by to visually inspect any computer-generated matches for confirmation and appropriate action At the end of the day, the score stood at Baltimore Ravens 34, New York Giants 7; computer matches 19, actual arrests 0; true matches, unknown The number of true matches is unknown because although the system indicated 19 matches, these could be false matches, also known as false positives or false alarms In this kind of law enforcement application, a computer match is ideally confirmed as a true match when the police officer, acting on the computer match, arrives on the scene, sees the suspected individual, and confirms the computer match Because police made no arrests based on computer matches, we do not know the number of true (and false) matches We should not assume that all 19 matches were false For example, a ticket scalper, identified as such by the facial recognition system, may have seen the police officers coming in his direction and given them the slip; or the police, not having actually witnessed him in the act of scalping tickets, may have directed him to leave the area, without making an arrest In 2002, the American Civil Liberties Union (ACLU) examined police records and other relevant information concerning Tampa s use of facial recognition in the Ybor City district The ACLU concluded that [t]he system has never correctly identified a single face in its database of suspects, let alone resulted in any arrests (Stanley and Steinhardt 2002) This January 28, 2001 incident was the first wide-scale use of facial recognition in the US, and it generated nationwide media coverage and much controversy as, in the eyes of many, the Super Bowl became the Snooper Bowl The Los Angeles Times headline read, Super Day for Big Brother: The creeping assault against privacy just turned plain creepy this week Wired News reported, Yes, Big Brother is watching your face Should we be concerned about the government s use of this technology One could argue that facial recognition is a standard identification technique that raises no special concerns After all, we look at each other s faces to recognize one another every day Police regularly use mugshots to identify criminals And we think nothing of being asked to display a photo ID to confirm our identity On the other hand, the use of a biometric facial recognition system such as that employed at the Super Bowl or at airports, like Boston s Logan, or at urban commercial districts where facial recognition has been deployed, like Oceanfront in Virginia Beach is different in certain respects from these more familiar uses and it has the potential to present greater risks as well as greater advantages Focusing on the risks, demonstrators, public advocacy groups, and at least one influential congressman have reacted to the use of facial recognition For example, protestors responded to Tampa Police using facial recognition in the Ybor City entertainment district, as the New York Times reported on July 15, 2001: Wearing masks and making obscene gestures at police cameras, about 100 people protested.

268 268 279 292 294 300 306 324 326 327

Analysis with PerformancePoint Server Analytic Charts Analytic Grids Analysis with ProClarity The ProClarity Professional Client The ProClarity Standard Client Summary 138 140 159 168 169 207 214

These devices come in two models:

8

Two-dimensional arrays The more prevalent packaging that permits the finger to be placed once and captured in a static fashion Single-row scanners The more compact packaging format that requires the finger to be moved, at a set rate, across a small scanning surface

7

Objectives 81 Types of Cable Communication Networks 82 Optical-Fiber Applications 83 The Interconnect Process Summary Questions Appendix A Appendix B Appendix C Appendix D Appendix E Appendix F Glossary References Index Proof and Discussion of Pertinent Equations Transmission Line Calculations Coaxial Cable System Powering Broadband Noise Combining Cascaded Ampli er Theory Answers to Odd-Numbered Problems

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To extract the minutiae and other feature data, the typical system goes through a multistep process First the image is acquired and then the fingerprint is segmented from the background This step involves edge detection and ridge flow algorithms such as two-dimensional Fourier Transforms for vertical and horizontal edge detection and Gabor filters to detect frequency and direction These algorithms take advantage of ridge properties such as the maximum grayscale values on a ridge are along a direction normal to the ridge flow

330 330 342 352 355 357 359 367 371 373 375 383 385 389 391

Planning Administration Console Planning Business Modeler Dimensions Models Forms and Reports Process Management Security and Roles Associations PerformancePoint Add-in for Excel Creating Reports and Forms Submitting Plan Data Summary 216 231 234 257 261 262 269 271 271 271 277 280

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