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3 OSAC 2024‐N‐0005

4 Standard Guide for Minimum

5 Training Recommendations of

6 Iris Image Examiners

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8 Facial & Iris Identification Subcommittee

9 Digital/Multimedia Scientific Area Committee (SAC)

10 Organization of Scientific Area Committees (OSAC) for Forensic Science

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29 OSAC Proposed Standard

30

31 OSAC 2024‐N‐0005

32 Standard Guide for Minimum Training

33 Recommendations of Iris Image

34 Examiners

35

36 Prepared by  

37 Facial & Iris Identification Subcommittee

38 Version: 1.0

39 February 2024

40

41

42 Disclaimer:

43

44 This OSAC Proposed Standard was written by the Facial & Iris Identification Subcommittee of the

45 Organization of Scientific Area Committees (OSAC) for Forensic Science following a process that

46 includes an open comment period. This Proposed Standard will be submitted to a standard

47 developing organization and is subject to change.

48 There may be references in an OSAC Proposed Standard to other publications under

49 development by OSAC. The information in the Proposed Standard, and underlying concepts and

50 methodologies, may be used by the forensic‐science community before the completion of such

51 companion publications.

52 Any identification of commercial equipment, instruments, or materials in the Proposed Standard

53 is not a recommendation or endorsement by the U.S. Government and does not imply that the

54 equipment, instruments, or materials are necessarily the best available for the purpose.

55    
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56 Standard Guide for Minimum Training Recommendations of Iris Image Examiners  

57 1. Scope

58 1.1 This guide defines a minimum set of topics for training of personnel who will conduct iris

59 image comparisons.

60 1.2 This standard does not purport to address all the safety concerns, if any, associated with

61 its use.  It is the responsibility of the user of this standard to establish appropriate safety and

62 health practices and determine the applicability of regulatory limitations prior to use.

63

64 2. Referenced Documents

65 2.1 ISO/IEC 2382‐37:2022 (en) Information technology – Vocabulary – Part 37:  Biometrics

66 2.2 ASTM E2917‐19a Standard Practice for Forensic Science Practitioner Training, Continuing

67 Education, and Professional Development Programs

68 2.3 [placeholder] ASTM WK72441 New Guide for Standard Guide for Developing Discipline

69 Specific Methodology for ACE‐V  

70

71 3. Terminology

72 3.1 Definitions:

73 3.1.1 Analysis, n:  the first step of the ACE‐V method. The assessment of an image to

74 determine suitability for comparison.

75 3.1.2 Comparison, n: the second step of the ACE‐V method; the examination of two or

76 more samples to establish similarities and dissimilarities.

77 3.1.3 Evaluation, n:  the third step of the ACE‐V method; where an examiner assesses

78 the value of the details observed during the analysis and comparison steps and reaches a

79 conclusion.  Ascertaining the value of dissimilarities and similarities between two images.

80 3.1.4 Iris Image, n:  an image of a human eye that contains the iris which constitutes a

81 biometric sample of the human eye.
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82 3.1.4.1 Discussion ‐ Iris images will typically include features of the periocular region such

83 as eyelids, eyebrows, eyelashes, and canthi.

84 3.1.5 Iris Recognition System, n:    an automated machine‐based system used to

85 compare images based on iris characteristics and quantitatively assess their similarity.

86 3.1.6 Iris Image Comparison, n:   an assessment of the similarities/dissimilarities of a

87 pair of iris images.

88 3.1.7 Iris Image Examiner, n:  a person who conducts iris image comparisons.

89 3.1.8 Verification, n: (1) the finalstep of the ACE‐V method; the review and independent

90 analysis of the conclusion of another examiner.    (2) In a biometric system, determining the

91 validity of a biometric claim.  

92 3.1.8.1 Discussion ‐ Verification may be followed by some level of review as specified by

93 agency policy.

94

95 4. Acronyms

96 4.1 ACE‐V:  Analysis, Comparison, Evaluation, and Verification  

97 4.1.1 Discussion  ‐  Methodology used by forensic practitioners primarily when

98 conducting feature comparisons.

99

100 5. Significance and Use

101 5.1 Achieving proficiency in iris comparison requirestraining.  Training must cover the

102 topics necessary to establish competency for an iris image examiner.  This document provides

103 minimum training topics for an iris image examiner curriculum.

104 5.2 The intended audience for this document includes all personnel involved in iris

105 image comparisons.

106 5.3 This document includes, as appendices, an example Recommended Reading list

107 and an example Training Program.

108
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109 6. Minimum Training for Iris Image Comparisons

110 6.1 History

111 6.1.1 Beliefs about the iris throughout history

112 6.1.2 Contributions made by individuals to iris recognition

113 6.1.3 Landmark events and major deployments

114

115 6.2 Biology

116 6.2.1 Embryological and postnatal development

117 6.2.2 Iris and periocular structure

118 6.2.3 Stability of iris patterns

119 6.2.4 Differences between irises; variations in iris texture intra (same person) and inter

120 (different people)

121 6.2.5 Biological distortions

122

123 6.3 Iris Image Recognition Systems  

124 6.3.1 Iris image acquisition

125 6.3.2 Image science basics  

126 6.3.3 Iris image science basics

127 6.3.4 Function and use of iris algorithms

128 6.3.5 Performance assessments of iris recognition systems

129

130 6.4 Human Iris Comparison

131 6.4.1 The relationship between human iris image comparison and iris recognition

132 systems

133 6.4.2 Visible iris and periocular features

134 6.4.3 Analysis, Comparison, Evaluation, and Verification (ACE‐V) methodology
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135 6.4.4 Value determination for comparison

136 6.4.5 Comparison of two images

137 6.4.6 Source opinions

138 6.4.7 Quality assurance

139

140 6.5 Iris Anomalies

141 6.5.1 Disease and temporary conditions

142 6.5.2 Traumatic injury

143 6.5.3 Drug and alcohol effects

144 6.5.4 Surgical effects

145 6.5.5 Patterned contacts/cosmetics enhancements

146 6.5.6 Iris Presentation Attack and Detection (PAD)

147 6.5.7 Generative Adversarial Network (GANS)/Deepfakes

148

149 6.6 Human Factors

150 6.6.1 Validation methods

151 6.6.2 Performance assessments

152 6.6.3 Bias

153 6.6.4 Reporting and testimony

154 6.6.5 Psychological stressors

155

156 6.7 Logic, Probability, and Statistics

157 6.7.1 Logic and Reasoning

158 6.7.2 Descriptive Statistics

159 6.7.3 Probability Theory  

160 6.7.4 Inferential Statistics

161 6.7.5 Reporting results
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162 6.8 Legal Considerations

163 6.8.1 Legal admissibility and landmark cases

164 6.8.2 Effective courtroom testimony

165 6.8.3 Comprehensive description of iris recognition and forensic comparison

166 concepts.

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186    
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187 APPENDIX

188 (Non Mandatory Information)

189 X1. Example Recommended Readings
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190 X.1 History

191 X.1.1 Bertillon, A. 1896. Signaletic Instructions:  Including the Theory and Practice of

192 Anthropometrical Identification.  (R.W. McClaughry, Trans.)  Chicago:  The Werner Company.  

193 Second Part, Chapter I., Section A, pp. 130 – 147

194 X.1.2 Daugman, J.  2001. “Iris Recognition”. American Scientist.  Sigma Xi, The

195 Scientific Research Society, vol. 89.  pp. 326‐329.

196 X.1.3 Daugman J. 2004. "How iris recognition works."  IEEE Trans. Circuits and Systems

197 for Video Technology 14(1), pp. 21 ‐ 30.

198 X.1.4 Duke‐Elder, S., Wybar, K.  1961.  The Anatomy of the Visual System. vol. 2, St.

199 Louis:  The C.V. Mosby Company. Chapters 1, 2

200 X.1.5 Flom, L., Safir, A.  1987.  Iris Recognition System.  US 4641349.  United States

201 Patent Office

202 X.1.6   Daugman, J.  1994.  Biometric Personal Identification System Based on Iris

203 Analysis.  US 5291560. United States Patent Office  

204 X.1.7 Matey, J., et al. 2020. Analysis of Iris Images in Nicholas Nixon: The Brown

205 Sisters, NIST Technical Note 2098.

206 X.1.8 Matsushita, M.  1999.  Iris Identification System and Iris Identification Method.  

207 US 005901238A.  United States Patent Office.

208 X.1.9 Wildes, R., et al., 1994. A System for Automated Iris Recognition, IEEE

209 Publication, Applications of Computer Vision, 1994 Workshop, pp. 121‐128

210

211 X.2 Biology

212 X.2.1 Aslam, T., Tan S., Dhillon, B.  2009. “Iris recognition in the presence of ocular

213 disease.”  Journal of The Royal Society, vol. 6, pp. 489‐493.

214 X.2.2 Boyd, A., Yadav, S., Swearingen T., Kuehlkamp, A., Trokielewicz, A., Benjamin, E.,

215 Maciejewicz, P., Chute, D., Ross, A., Flynn, P., Bowyer, K., Czajka, A. 2020. “Post‐Mortem Iris
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216 Recognition — A Survey and Assessment of the State of the Art,” in IEEE Access, vol. 8, pp.

217 136570‐136593

218 X.2.3 Gold, D., Lewis, A. (Eds.) 2011.  Clinical Eye Atlas.  2nd ed.  New York, NY:  Oxford

219 Press.  pp. 396‐400.

220 X.2.4 Johnson, M., Yambay, D., Rissacher, D., Holsopple, L., Schuckers, S. 2018. A

221 longitudinal study of iris recognition in children. 2018 IEEE 4th International Conference on

222 Identity, Security, and Behavior Analysis (ISBA).  

223 X.2.5 Roizenblatt, R., Schor, P., Dante, F., Roizenblatt, J., Belfort, R.  2004. “Iris

224 recognition as a biometric method after cataract surgery.”  BioMedical Engineering Online.  Vol.

225 3.   

226 X.2.6 Saerwein, K., Saul, T., Steadman, D., Boehnen, C.  2017. “The Effect of

227 Decomposition on the Efficacy of Biometrics for Positive Identification.”  Journal of Forensic

228 Sciences.  vol. 62, no. 6.  pp. 1599‐1602.

229 X.2.7 Snell, R.S, and Lemp, M.A. (eds), 1998.  Clinical Anatomy of the Eye.  2nd ed.  

230 Oxford UK:  Blackwell Science [ISBN 0‐632‐04344‐X].  Chapters 1, 3, 5, 6.

231 X.2.8 Trokielewicz, M., Czajka, A., Maciejewicz, P.  2019. “Iris Recognition After Death”.  

232 IEEE Transactions on Information Forensics and Security.  vol. 14, no. 6, pp. 1501‐1514, June

233 2019, doi: 10.1109/TIFS.2018.2881671.

234 X.2.9 Safa BN, Bahrani Fard MR, Ethier CR. 2022. “In vivo biomechanical assessment of

235 iridial deformations and muscle contractions in human eyes”. J R Soc Interface.

236 19(192):20220108. doi: 10.1098/rsif.2022.0108. Epub 2022 Jul 6. PMID: 35857902; PMCID:

237 PMC9257589.

238 X.2.10 Trokielewicz M., Czajka A., Maciejewicz P. 2016. “Implications of Ocular

239 Pathologies for Iris Recognition Reliability,” Image and Vision Computing, vol. 58, pp. 158–167,

240 Elsevier, doi: 10.1016/j.imavis.2016.08.001.

241 X.3 Iris Image Recognition Systems  

242 X.3.1 Daugman, J.  2004. “How Iris Recognition Works.”  IEEE Transactions on Circuits

243 and Systems for Video Technology.  vol. 14, no. 1
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244 X.3.2 Daugman, J.  2007. “New Methods in Iris Recognition”.  IEEE Transactions of

245 Systems, Man, and Cybernetics – Part B: Cybernetics.  vol. 37, no. 5.

246 X.3.3 Hollingsworth, K., Bowyer, K., Flynn, P.  2009. “Pupil dilation degrades iris

247 biometric performance.”  Computer Vision and Image Understanding.  vol. 113. pp. 150‐157.   

248 X.3.4 NIST.  “IREX 10:  Identification Track.” https://pages.nist.gov/IREX10/

249 X.3.5 Quinn, G., Grother, P., Matey, J.  2019.  IREX IX Part Two:  Multispectral Iris

250 Recognition.  NISTIR 8252 pg. 1‐2, Section 1, Section 2.2, Section 3.

251

252 X.4 Human Iris Comparison

253 X.4.1 Chen, J., Feng, S., Chen, D., Flynn, P.  2016. “Iris Recognition Based on Human‐

254 Interpretable Features.”  IEEE Transactions on Information Forensics and Security. vol.11, no. 7,

255 pp.1556‐6013.

256 X.4.2 Edwards, M., Cha, D., Krithika, S., Johnson, M., Parra, E.J.  2016.” Analysis of iris

257 surface features in populations of diverse ancestry.” London: Royal Society Publishing, vol 3.,

258 issue 1

259 X.4.3 Hollingsworth, K. P., Darnell, S. S., Miller, P. E., Woodard, D. L., Bowyer, K. W., &

260 Flynn, P. J. (2011). Human and machine performance on periocular biometrics under near‐

261 infrared light and visible light. IEEE transactions on information forensics and security, 7(2), 588‐

262 601.

263 X.4.4 McGinn, K., Tarin, S., Bowyer, K. W.  2013. “Identity verification using iris images:

264 performance of human examiners.” IEEE Sixth International Conference on Biometrics: Theory,

265 Applications and Systems (BTAS) pp. 1‐6.

266 X.4.5 Muron, A., Pospisil, J.  2000.  The Human Iris Structure and Its Usages. In Acta

267 Univ. Palacki. Physica, vol. 39. pp. 87‐91

268 X.4.6 Shen, F., and Flynn, P.  2012. “Iris Matching by Crypts and Anti‐crypts.”  IEEE   

269 Conference on Technologies for Homeland Security.  pp. 208‐213.

270 X.4.7 Vanderkolk, J. 2009. R Forensic Comparative Science: Comparative Quantitative

271 Source Determination of Unique Impressions, Images, and Objects. London: Elsevier Academic
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272 Press.  Chapter 6.  

273 X.4.8 Moreira D., Trokielewicz M., Czajka A., Bowyer K. and Flynn P. 2019.

274 "Performance of Humans in Iris Recognition: The Impact of Iris Condition and Annotation‐

275 Driven Verification," IEEE Winter Conference on Applications of Computer Vision (WACV), pp.

276 941‐949, doi: 10.1109/WACV.2019.00105

277

278 X.5 Iris Anomalies

279 X.5.1 Boyd, A., Fang, Z., Czajka, A., Bowyer, K. 2020. “Iris Presentation Attack

280 Detection: Where Are We Now?” Pattern Recognition Letters, Vol. 138, pp. 483–489

281 X.5.2 Czajka, A., Bowyer, K. 2018. “Presentation Attack Detection for Iris Recognition:

282 An Assessment of the State of the Art,” ACM Computing Surveys, Vol. 51, No. 4, pp. 86:1–86:35

283

284 X.6 Human Factors

285 X.6.1 Growns, B., Martire, K. 2020. “Human factors in forensic science: The cognitive

286 mechanisms that underlie forensic feature‐comparison expertise.” Forensic Science

287 International: Synergy. Vol 2., pp 148‐153.

288 X.6.2 National Commission on Forensic Science.  2015. Ensuring That Forensic Analysis

289 is Based Upon Task‐Relevant Information.  

290 https://www.justice.gov/archives/ncfs/page/file/641676/download

291 X.6.3 OSAC.  2020. Human Factors in Validation and Performance Testing of Forensic

292 Science.  Technical Series Publication 0004.  https://doi.org/10.29325/OSAC.TS.0004

293 X.6.4 Dror, I. E. (2020). Cognitive and Human Factors in Expert Decision Making: Six

294 Fallacies and the Eight Sources of Bias. Analytical Chemistry, 92(12), 7998–8004.

295 https://doi.org/10.1021/acs.analchem.0c00704

296
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297 X.7 Logic, Probability, and Statistics

298 X.7.1 Center for Statistics and Applications in Forensic Evidence.  Statistics for Forensic

299 Practitioners.  https://forensicstats.org/course‐statistics‐for‐forensic‐practitioners/

300 X.7.2 Daugman, J.  2003. “The importance of being random:  statistical principles of iris

301 recognition”.  Pattern Recognition. vol. 36, pp. 279‐291

302 X.7.3 European Network of Forensic Science Institutes. 2015.  Guideline for Evaluative

303 Reporting in Forensic Science. https://enfsi.eu/wp‐content/uploads/2016/09/m1_guideline.pdf

304 X.7.4 Lindley, D. 2014. Understanding Uncertainty. 2nd ed. Hoboken, NJ: Wiley.  

305 Chapters 1 and 6.

306 X.7.5 Moore, S., McCabe, G., Craig, B. 2016. Introduction to the Practice of Statistics.  

9th 307 ed. Virginia:  Macmillan Learning. Chapters 1, 2 and 4

308

309 X.8 Legal Considerations

310 X.8.1 Chisum, J., Turvey, B. 2004.  Crime Reconstruction.  Elsevier Academic Press.

311 Chapter 13.   

312 X.8.2 Executive Office of the President, President’s Council of Advisors on Science and

313 Technology (PCAST).  2016.  Panel on Forensic Science, Forensic Science in Criminal Courts:

314 Ensuring Scientific Validity of Feature‐Comparison Methods.  

315 https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/PCAST/pcast_forens

316 ic_science_report_final.pdf  

317 X.8.3 Holder, E., Robinson, L., Laub, J. 2011. The Fingerprint Sourcebook. US

318 Department of Justice, Office of Justice Programs, National Institute of Justice. Chapter 12, skip

319 sections 12.2.4, 12.2.14, 12.2.16.2, 12.2.17; Chapter 13, sections 13.1 (skip 13.2.2.3), 13.2,

320 13.3,1, 13.5; Chapter 15   

321    
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322 APPENDIX

323 (Non Mandatory Information)

324 X2.  EXAMPLE TRAINING PROGRAM

325 These topics are intended as a suggested starting point for the development of a training

326 program for iris image examiners.   

327 X.9 History

328 X.9.1 Beliefs about the iris throughout history

329 X.9.2 Iris divination

330 X.9.2.1 Iridology

331 X.9.3 Contributions made by individuals to iris recognition

332 X.9.3.1 Alphonse Bertillion

333 X.9.3.2 Frank Burch

334 X.9.3.3 J.H. Doggart

335 X.9.3.4 F.H Adler

336 X.9.3.5 Leonard Flom and Aran Safir

337 X.9.3.6 John Daugman

338 X.9.3.7 Richard P. Wildes

339 X.9.3.8 Mitsuji Matsushita

340 X.9.4 Landmark events and major deployments

341 X.9.4.1 Emirates ID

342 X.9.4.2 United Kingdom Project Iris Recognition Immigration System (IRIS)

343 X.9.4.3 Unique Identification Authority of India’s (UIDAI) Aadhaar system

344 X.9.4.4 Canadian Passenger Accelerated Service System (CANPASS)

345 X.9.4.5 CLEAR

346 X.9.4.6 Identity for All in Africa (ID4Africa)

347 X.9.4.7 The Afghan Girl
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348 X.9.4.8 The Brown Sisters

349

350 X.10 Biology

351 X.10.1 Embryological and postnatal development

352 X.10.1.1   Embryological development

353 X.10.1.2   Iris layer formation and timing

354 X.10.1.3   Changes that occur postnatal

355 X.10.2 Iris and periocular structure

356 X.10.2.1   Understand the mechanical structure of the iris and basis of the persistence of

357 the iris structure.

358

359 X.10.3 Stability of iris patterns

360 X.10.3.1   Changes due to aging

361 X.10.3.2   Changes due to disease and injury

362 X.10.3.3   Changes that occur postmortem

363 X.10.4 Differences between iris patterns, for same and for different people

364 X.10.4.1   Understand the genetic and epigenetic influences on the resulting appearance

365 of the iris, with specific emphasis on:

366 X.10.4.2   The biological origin and nature of iris variability

367 X.10.4.3   Similarities and differences between monozygotic twin iris structures

368 X.10.5 Biological distortions

369 X.10.5.1   Pupil dilation and contraction

370

371 X.11 Iris Image Recognition Systems

372 X.11.1 Iris image acquisition

373 X.11.1.1 Describe methods of iris image capture (e.g., near‐IR)

374 X.11.1.2 Understand control measures needed to achieve quality iris images
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375 X.11.1.3 Understand procedures for addressing missing eyes, injuries, and image

376 recapture

377 X.11.2 Image science basics

378 X.11.2.1 Photonics

379 X.11.2.2 Wavelengths  

380 X.11.2.3 Spectral reflectance

381 X.11.2.4 Optics

382 X.11.2.5 Sensors

383 X.11.3 Iris image science basics

384 X.11.3.1 Iris albedo

385 X.11.3.2 Specularities

386 X.11.4 Function and use of iris algorithms

387 X.11.4.1 Phase structure algorithms (Iris2Pi)

388 X.11.4.2 Discrete features algorithms

389 X.11.4.3 Deep Neural Network algorithms (currently uninterpretable inferences)

390 X.11.4.4 Understand processes related to acquisition, searching, storage, retrieval,

391 identification, and reporting of iris image records

392 X.11.4.5 Understand system quality controls to ensure completeness, image quality, and

393 data integrity

394 X.11.5 Performance assessments of iris recognition systems

395 X.11.5.1.1 IREX‐10

396 X.11.5.1.1.1 Black box studies of algorithms

397 X.11.5.1.2 Effect of pupil dilation on algorithm results

398 X.11.5.1.3 Differences that visible light images, near infrared images, and other

399 multispectral images have on results

400

401 X.12 Human Iris Comparison

402 X.12.1 The relationship between human iris image comparison and iris recognition

403 systems
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404 X.12.2 Visible iris and periocular features

405 X.12.3 Analysis, Comparison, Evaluation, and Verification (ACE‐V) methodology

406 X.12.4 Value determination for comparison

407 X.12.4.1 Understand the assessment of quality, quantity, and rarity of features

408 X.12.4.2 Understand the concept of sufficiency as it relates to drawing conclusions

409 X.12.4.3 Understand and demonstrate the ability to properly determine correct

410 orientation and difference between right and left irises

411 X.12.4.4 Understand and demonstrate how to document observations

412 X.12.5 Comparison of two images

413 X.12.5.1 Understand how to select an effective target group  

414 X.12.5.2 Understand and demonstrate how to assess the discriminability of features

415 X.12.5.3 Understand and demonstrate the ability to distinguish between an apparent

416 dissimilarity and an actual difference in iris texture, for example:

417 X.12.5.3.1 Pupil dilation

418 X.12.5.3.2 Effect of aging, disease, surgery, post‐mortem changes

419 X.12.5.3.3 Perceived differences caused by  wavelength changes

420 X.12.6 Source opinions

421 X.12.6.1 Exclusion

422 X.12.6.2 Strong Support for  exclusion

423 X.12.6.3 Support for exclusion

424 X.12.6.4 Inconclusive

425 X.12.6.5 Support for common source

426 X.12.6.6 Strong support for common source

427 X.12.7   Quality assurance

428 X.12.7.1 Understand the measures that should be taken to verify opinions

429 X.12.7.2 Understand the types of possible verifications:  peer review, blind verification,
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430 multiple verifiers, and group consensus

431 X.12.7.3 Understand the issues that may contribute to erroneous conclusions and

432 safeguards that can help minimize their occurrence

433 X.12.8 Software tools supporting human examination of iris images

434 X.12.8.1 Inversion of geometric distortions caused by differences in pupil dilation

435 X.12.8.2 Rescaling

436 X.12.8.3 Rotation

437 X.12.8.4 Contrast Normalization

438

439 X.13 Iris Anomalies

440 X.13.1 Disease and temporary conditions

441 X.13.2 Traumatic injury

442 X.13.3 Drug and alcohol effects

443 X.13.4 Surgical effects

444 X.13.5 Patterned contacts/cosmetic enhancements

445 X.13.6 Iris Presentation Attacks (IPAD)

446 X.13.7 Generative Adversarial Network (GAN) images/Deepfake

447
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448 X.14 Human Factors for Iris Image Examiners

449 X.14.1 Validation methods

450 X.14.1.1 Understand the role of human judgements in forensic science methods and

451 necessity for empirical testing of human accuracy.

452 X.14.1.2 Black Box Studies

453 X.14.1.3 White Box Studies

454 X.14.2 Performance assessments

455 X.14.2.1 Understand the importance of examiner training, proficiency testing, quality

456 assurance, and professional development.

457

458 X.14.3 Bias

459 X.14.3.1 Understanding and mitigating effects of bias in comparisons

460 X.14.3.1.1 Cognitive bias

461 X.14.3.1.2 Contextual bias

462 X.14.3.1.3 Confirmation bias

463 X.14.4 Reporting and testimony

464 X.14.4.1 Understand the importance of using justifiable, scientifically based statements

465 X.14.4.2 Understand the importance of transparency

466 X.14.4.3 Understand the importance of using common sense terminology

467 X.14.5 Psychological stressors

468 X.14.5.1 Understand the impact psychological well‐being on examiners

469 X.14.5.2 Time pressure and fatigue

470 X.14.5.3 Exposure to traumatic events (e.g., violent crimes, disasters)

471 X.14.5.4 Adversarial environments

472

473 X.15 Logic, Probability, and Statistics

474 X.15.1 Logic and reasoning

475 X.15.1.1 Understand how inferences are formed using deductive, inductive, and
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476 abductive logic.

477 X.15.1.2 Understand the different circumstances in which deductive, inductive, and

478 abductive logic are utilized as well as the strengths and limitations associated with these

479 resulting inferences.

480 X.15.2 Descriptive statistics

481 X.15.2.1 Understand the concepts of variables, data, frequency distributions, and

482 statistics

483 X.15.2.2 Understand the statistics and displays for describing the central tendency and

484 variability of data.

485 X.15.3 Probability Theory  

486 X.15.3.1 Understand the axioms of mathematical probability and the definition of a

487 probability function, and it’s cumulative.

488 X.15.3.2 Understand the definition of conditional probability and why transposing the

489 events or proposition in a conditional probability is not generally correct

490 X.15.3.3 Understand the difference between a likelihood and a probability

491 X.15.3.4 Understand the relationship between probabilities and odds

492 X.15.3.5 Understand the components of the odds form of Bayes’ rule for binary

493 variables and their relationship  

494 X.15.3.6 Understand the definition of combinatorics: how feature comparison

495 combinations generate probabilities.  

496 X.15.4 Inferential Statistics  

497 X.15.4.1 Understand the concept of a “probability distribution and its parameters”

498 X.15.4.2 Understand the difference between a sample statistic and a population

499 parameter, including the estimation of a population proportion from a sample proportion

500 X.15.4.3 Understand and be able to explain the differences between the following

501 terms and their use when describing the performance of an analytical technique:   

502 X.15.4.3.1 Sensitivity (True positive rate, likelihood of correct detection of a match)

503 X.15.4.3.2 Specificity (True positive rate, Likelihood of correct rejection of a non‐match)
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504 X.15.4.3.3 False positive rate

505 X.15.4.3.4 False negative rate

506 X.15.4.3.5 Positive predictive value

507 X.15.4.3.6 Negative predictive value

508 X.15.4.3.7 False positive discovery rate

509 X.15.4.3.8 False negative discovery rate

510 X.15.5 Reporting Results

511 X.15.5.1 Understand the various methods of expressing the weight of evidence as they

512 relate to iris image evidence.

513 X.15.5.1.1 Posterior probability

514 X.15.5.1.2 Likelihood ratio

515 X.15.5.1.3 Bayes Factor
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516 X.16 Legal Considerations

517 X.16.1 Legal admissibility and landmark cases

518 X.16.1.1 U.S. vs. Frye (1923)

519 X.16.1.2 Daubert vs. Merrell Dow Pharmaceuticals (1993)

520 X.16.1.3 Kumho Tire vs. Carmichael (1999)

521 X.16.1.4 Brady vs. Maryland (1963)

522 X.16.2 Effective courtroom testimony

523 X.16.2.1 Understand the importance of verbal and non‐verbal communication

524 X.16.2.2 Understand the importance of personal appearance

525 X.16.2.3 Understand the importance of vocal volume and inflection

526 X.16.2.4 Understand the jurisdiction’s rules about reference to notes or other materials

527 X.16.3 Comprehensive description of iris recognition and forensic comparison

528 concepts

529 X.16.3.1 Describe how iris algorithms function

530 X.16.3.2 Describe the comparison process of the evidence

531 X.16.3.2.1 Describe the strengths and weaknesses of ACE‐V

532 X.16.3.2.2 Describe how tolerance is established for differences in appearance during

533 analysis

534 X.16.3.3 Describe the research that measures the rarity of iris features

535 X.16.3.4 Describe the research that studies the persistence of iris features

536 X.16.3.5 Describe how likelihood ratio research is used to support conclusions

537 X.16.3.6 Describe how human factors can affect decision‐making