Igor Janos（Innovatrics）：利用合成数据突破潜在指纹识别的极限.pdf

1、IFPC 2025 ConferenceIgor JnoHead of R&DPushing the limits of latent fingerprint identification with synthetic data 2MotivationImages of latent fingerprints are scarceOften they are confidentialpart of an investigation(ongoing or closed)Matching pairs(latent-plain)are difficult to get3Known Fingerpri

2、ntsAcquired using ink or digital scannersControlled environmentConstant DPILess diverse4Unknown FingerprintsDiverse backgroundsPartial informationAcquired using different techniques5Annotating latent fingerprints is difficultHaving forensic experts do the annotation is expensiveLaymen will not alway

3、s be consistent6Our Approach7Our ApproachTrain on purely synthetic latent imagesWe acknowledge lot of research on synthetic fingerprint generationCycle-GAN-based approachesDiffusion-based approachesProcedural approaches8TrainingPredict the same features from plain and latent imagesPlain Feature Extr

4、actorLatent Feature ExtractorLoss between featuresGround TruthPrediction9Training DataDomain A(Plains)Domain B(Latents)Produce big bag of image pairsAugmentationGANsDiffusionWe have no real matching pairsUnpaired image translation10Training DataReal PlainsReal Latents(SD302)Latents contain a lot les

5、s information than the plains11Submission 112Reconstructed PlainsOur Submission 1Masked by random polygonAdded random noiseReal PlainsP LSynthetic LatentsLatent DomainDiscriminatorL PConsistency Loss13Reconstructed LatentsOur Submission 1Real Latents still containa lot less informationReal LatentsL

6、PSynthetic PlainsPlain DomainDiscriminatorP LConsistency Loss14Reconstructed LatentsOur Submission 1Plain Discriminator is forcingthe(LP)model to make up informationReal LatentsL PSynthetic PlainsPlain DomainDiscriminatorP LConsistency Loss15Reconstructed LatentsOur Submission 1I