AI and Deep Learning in Biometric Security: Trends, Potential, and Challenges


Deep Learning-Based Hyperspectral Multimodal Biometric Authentication System Using Palmprint and Dorsal Hand VeinINTRODUCTIONDEVICE DESIGNSYSTEM IMPLEMENTATIONROI ExtractionHyperspectral Palmprint ROI ExtractionHyperspectral Dorsal Hand Vein ROI ExtractionFeature ExtractionFeature Fusion and MatchingEXPERIMENTSMultimodal Hyperspectral Palmprint and Dorsal Hand Vein DatasetOptimal Pattern and Band SelectionMultimodal IdentificationMultimodal VerificationComputational Complexity AnalysisCONCLUSIONSACKNOWLEDGEMENTSREFERENCESCancelable Biometrics for Template ProtectionINTRODUCTIONTEMPLATE PROTECTIONROLE OF DEEP LEARNING APPROACHES IN BIOMETRICSRELATED WORK: TEMPLATE PROTECTIONBiometric EncryptionBiometric CryptosystemsCancelable BiometricsDeep Learning-Based Cancelable TechniquesDeep Learning versus Non-deep Learning Cancelable TechniquesPERFORMANCE MEASURES AND DATASETS IN CANCELABLE BIOMETRICSPerformance Measures for Non-invertibility AnalysisPerformance Measures for Unlinkability AnalysisPerformance Measures for System Usability AnalysisPerformance Measures for Revocability AnalysisDatabases Used in Cancelable BiometricsCOMPARATIVE PERFORMANCE ANALYSIS: CANCELABLE BIOMETRICSCONCLUSIONS AND FUTURE PROSPECTIVE OF DEEP LEARNING IN BIOMETRICSREFERENCESOn Training Generative Adversarial Network for Enhancement of Latent FingerprintsINTRODUCTIONRELATED WORKPROPOSED ALGORITHMProblem Formulation and Objective FunctionTraining Data PreparationNetwork Architecture and Training DetailsPERFORMANCE EVALUATIONDatabases and Tools UsedEvaluation CriteriaRESULTS AND ANALYSISCHALLENGES OBSERVEDCONCLUSIONSACKNOWLEDGEMENTSREFERENCESDeepFake Face Video Detection Using Hybrid Deep Residual Networks and LSTM ArchitectureINTRODUCTIONRELATED WORKCategories of Face ManipulationsDeepFakes DetectionPROPOSED DEEPFAKE VIDEOS DETECTION FRAMEWORKConvolutional Neural Networks (CNNs)Long Short-Term Memory (LSTM)Residual Neural Network (ResNet)EXPERIMENTSDatasetsFigures of MeritExperimental ProtocolExperimental ResultsCHALLENGES AND FUTURE RESEARCH DIRECTIONSCONCLUSIONSNOTESREFERENCESMulti-spectral Short- Wave Infrared Sensors and Convolutional Neural Networks for Biometric Presentation Attack DetectionINTRODUCTIONDEFINITIONSRELATED WORKSPROPOSED PAD METHODHardware: Multi-Spectral SWIR SensorSoftware: Multi-Spectral Convolutional Neural NetworksMulti-Spectral Samples Pre-ProcessingCNN ModelsScore Level FusionEXPERIMENTAL SETUPDatabaseEvaluation MetricsExperimental ProtocolEXPERIMENTAL EVALUATIONBaseline: Handcrafted RGB ConversionInput Pre-Processing OptimisationFinal Fused SystemCONCLUSIONS AND FUTURE RESEARCHACKNOWLEDGEMENTSREFERENCESAl-Based Approach for Person Identification Using ECG BiometricINTRODUCTIONECG AND RELATED WORKMETHODOLOGY ADOPTEDFeature ExtractionCLASSIFIERArtificial Neural Network (ANN)Support Vector Machine (SVM)EXPERIMENTS AND RESULTSCONCLUSIONSREFERENCESCancelable Biometric Systems from Research to RealityINTRODUCTIONCANCELABLE BIOMETRIC SYSTEMS: INTRODUCTION AND REVIEWConventional Template Transformation TechniquesRole of Deep Learning in Biometrics and Need for PrivacyNeutral Network-Based Template Transformation TechniquesEXPERIMENTAL REPORTINGREAL-LIFE CHALLENGES FOR APPLICATIONS OF CANCELABLE BIOMETRIC SYSTEMSCONCLUSIONS AND FORESIGHTSREFERENCESGender Classification under Eyeglass Occluded Ocular RegionINTRODUCTIONOur ContributionsRELATED WORKSVisible SpectrumNear-Infra-Red SpectrumVisible and Near-Infra-Red SpectrumMulti-Spectral ImagingDATABASEPROPOSED METHODSpectral Bands SelectionFeature ExtractionClassificationEXPERIMENTS AND RESULTSExperimental Evaluation ProtocolEvaluation 1: Without-Glass v/s Without-GlassIndividual Band ComparisonFused Band ComparisonEvaluation 2: Without-Glass v/s With-GlassIndividual Band ComparisonFused Band ComparisonCONCLUSIONSACKNOWLEDGEMENTREFERENCESInvestigation of the Fingernail Plate for Biometric Authentication using Deep Neural NetworksINTRODUCTIONMotivation and Scope of Present WorkRELATED WORKSAMPLE ACQUISITION AND ROI EXTRACTIONSample AcquisitionROI ExtractionFEATURE EXTRACTIONTransfer Learning using AlexNetTransfer Learning using ResNet-18Transfer Learning using DenseNet-201MULTIMODAL SYSTEM DESIGNScore-Level FusionRank-Level FusionLogistic Regression MethodMixed Group RankInverse Rank PositionNonlinear Weighted MethodsEXPERIMENTS, RESULTS, AND ANALYSESPerformance of Fingernail Plates in Verification SystemsPerformance of Fingernail Plates in Unimodal Verification SystemsPerformance of Fingernail Plates in Multimodal Verification SystemsPerformance of Fingernail Plates in Identification SystemsPerformance of Fingernail Plates in Unimodal Identification SystemsЭ.6.2.2 Performance of Fingernail Plates in Multimodal Identification SystemsCHALLENGES AND SCOPE OF FINGERNAIL PLATES IN BIOMETRICSCONCLUSIONS AND FUTURE SCOPEREFERENCESFraud Attack Detection in Remote Verification Systems for Non-enrolled UsersINTRODUCTIONRELATED WORKRemote Authentication Framework Using BiometricsImage Manipulation and Deep Learning TechniquesFAKE ID CARD DETECTION FOR NON-ENROLLED USERSDatabasesHand-Crafted Feature Extraction (BSIF, uLBP, and HED)Automatic Feature Extraction (CNN)EXPERIMENTS AND RESULTSFeature Extraction ClassificationClassification Using CNN AlgorithmsSmall-VGG Trained from ScratchPre-trained VGG16 Model and BottleneckPre-trained VGG16 Model and Fine-TuningCONCLUSIONSACKNOWLEDGEMENTREFERENCESIndexing on Biometric DatabasesINTRODUCTIONINDEXING FACIAL IMAGESPredictive Hash CodeINDEXING FINGERPRINT IMAGESCoaxial Gaussian Track CodeINDEXING FINGER-KNUCKLE PRINT DATABASEBoosted Geometric HashingINDEXING IRIS IMAGESIndexing of Iris Database Based on Local FeaturesINDEXING SIGNATURE IMAGESKD-Tree-Based Signature Database IndexingCONCLUSIONREFERENCESIris Segmentation in the Wild Using Encoder-Decoder-Based Deep Learning TechniquesINTRODUCTIONDEEP LEARNING FOR SEGMENTATIONRELATED WORKNon-Deep Learning-Based MethodologiesDeep Learning-Based MethodologiesDATA SETS AND EVALUATION METRICSData sets. CASIAPerformance MetricsEXPERIMENTATIONCHALLENGES IDENTIFIED AND FURTHER DIRECTIONCONCLUSIONACKNOWLEDGEMENTSREFERENCESPPG-Based Biometric RecognitionINTRODUCTIONPHOTOPLETHYSMOGRAM (PPG)LITERATURE REVIEWMULTI-FEATURE APPROACH FOR PPG BIOMETRICCLASSIFICATIONEXPERIMENTS AND RESULTSCONCLUSIONSREFERENCESCurrent Trends of Machine Learning Techniques in Biometrics and its ApplicationsINTRODUCTIONBiometric SystemsBrain StrokeFace RecognitionMotivation to Machine Learning TechniquesRELATED WORKReview on Brain StrokeReview on Face RecognitionBrain Stroke Prediction SystemImage AcquisitionPre-processingFeature ExtractionClassification Using Machine Leaning AlgorithmsDecision TreeArtificial Neural NetworkSupport Vector MachineDeep Learning with CNNConstruction of Convolutional Neural NetworkFace-Recognition SystemDISCUSSION AND RESULTSPerformance of Brain StrokePerformance of Face RecognitionFUTURE SCOPECONCLUSIONREFERENCES
 
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