Austin Hom（NIST）：FRTE 视频人脸识别（FIVE）.pdf

1、1NIST FACE IN VIDEO EVALUATION(FIVE)NIST FACE IN VIDEO EVALUATION(FIVE)AUSTIN HOM,PATRICK GROTHER,MEI NGAN AUSTIN HOM,PATRICK GROTHER,MEI NGAN NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGYNATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGYIFPC April 1st,2025April 1st,202521:1 VERIFICATION1:N SEARCHTWINS

2、 DISAMBIGUATIONFACE IN VIDEO 2024MORPH DETECTIONPADAGE ESTIMATIONTWO PEOPLE IN ONE FACE?SUBVERSIVE PHOTO?HOW OLD?OLD ENOUGH?SAME PERSON OR NOT?WHO?WHERE?WHEN?SAME PERSON,OR TWIN?1:N ON NON-COOP PEOPLEQUALITY+DIAGNOSTICSHOW BAD IS THIS PHOTOFRTEFACE RECOGNITIONRECOGNITION TECHNOLOGY EVALUATIONFATEFAC

3、E ANALYSIS ANALYSIS TECHNOLOGY EVALUATIONRECOGNITION:WHO IS IN AN IMAGENISTS Open BenchmarksBenchmarks are:Independent Free Regular Fast Repeatable Fair Black box IP-protecting Open globally Large-scale Sequestered datasets Statistically robust Public Transparent Extensible ABSOLUTE ACCU RELATIVE AC

4、CUANALYSIS:ABOUT IN AN IMAGE3Challenges of FR in video Pose Compound rotation of head to optical axis Resolution Range to subject Legacy camera Adverse compression for storage or transmission Motion blur But Multiple frames Challenges for FR4NIST Face in Video Evaluation(FIVE)2024Assessment of the s

5、tate-of-the-art of 1:N face recognition(FR)on video sequences(and relative improvements since FIVE 2015)Assessment of FR on videos with low qualityLow resolution including compressed video and long range imaging affected by atmospheric turbulenceElevated cameras resulting in high look-down anglePass

6、ively observed subjects who at no point face the camera directlyFace detection in wide field-of-view imageryAbsolute accuracyComparative accuracy of algorithmsComparative computational costThreshold calibration-ability to target specific false positive identification ratesGoalsRe-identificationAnoma