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Analyzing and Reducing the Damage of Dataset Bias to Face Recognition with Synthetic Data

Explore the damage caused by dataset bias to face recognition systems and propose a solution using synthetic face images to improve generalization and performance in less controlled environments. Quantify the impact of biases on face recognition and discuss the benefits of pre-training with synthetic data.

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Analyzing and Reducing the Damage of Dataset Bias to Face Recognition with Synthetic Data

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  1. AnalyzingandReducingtheDamageof Dataset Bias to Face Recognition withSynthetic Data Adam Kortylewski, Bernhard Egger, Andreas Schneider, Thomas Gerig, Andreas Morel-Forster, Thomas Vetter

  2. Successof AI at Face Recognition • FR workswell(in rathercontrolledenvironments): • FR needstobeimproved in lesscontrolledenvironments: • Surveillance, disguisedfaces, … ATMs Airports Phones

  3. Deep Face Recognition Systems SufferfromDataset Bias • Deepfacerecognitionsystems do not generalizewell(topreviouslyunseenviews, partial occlusion, context, …) • FR systemsneedtogeneralizetoout-of-distribution samples • In thiswork, weaimto: • Quantifythedamagefromdatasetbias • Undothedamagefromdatasetbias

  4. Difficultyofquantifyingthedamageofdatasetbias • Scalarperfromancemeasureprovidesonly limited informationaboutthegeneralizationabilityof a CNN architecture • Cannotsystematicallyanalyze „weakspots“ • Weneedtostudygeneralizationperformanceas a functionofnuisance variables

  5. Synthetic Face Image Generation • Weusethe 3DMM andcomputergraphicstosynthesizefaceimages • https://github.com/unibas-gravis/parametric-face-image-generator Blanz and Vetter (1999). A Morphable Model for the Synthesis of 3D Faces, SIGGRAPH Paysan, Knothe, Amberg, Romdhani and Vetter (2009) A 3D Face Model for Pose and Illumination Invariant Face Recognition, AVSS

  6. QuantifyingtheDamageofBiases in the Pose Distribution • VGG-16 generalizesbetterthanAlexNettounseenfaceposes. Kortylewski, et al. "Empirically analyzing the effect of dataset biases on deep face recognition systems.“, 2018.

  7. Pose Bias SpecificforFacial IDs • DNNs cannotdisentangleheadposeandfacialidentity. Kortylewski, et al. "Empirically analyzing the effect of dataset biases on deep face recognition systems.“, 2018.

  8. UndoingtheDamageof Dataset Bias withSynthetic Data • Exp. setup: Pre-train withsyntheticdataandfine-tune with real-worlddata

  9. Whydoespre-training withsyntheticdatahelp? • Becauseofthe additional variability in thefaceposeandnumberoffacialidentities

  10. Summary • Deepfacerecognitionsystemssufferfromdatasetbias • Usingsyntheticfaceimageswecan: • Quantifythedamageofdatasetbias on facerecognitionperformance • Enhancethedataefficiencyandgeneralizationperformanceof FR systems

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