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Paper Citation Record · LEDGER

Bridging the gap in FER: addressing age bias in deep learning

As of 19 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 0 inbound Pith citation observations for arXiv:2507.07638.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.07638 v1

Coverage vector

measured 66 of 66 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:42:47.222958Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

66 of 66 outbound references displayed

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External citation measurements

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Outbound references

Observation 6f9c5314-efda-4d6d-aff7-f2f2e1fdcd40 · outbound

This paper cites Peekinginsidetheblack-box:Asurveyonexplainableartificialintelligence(xai).

Bridging the gap in FER: addressing age bias in deep learning Peekinginsidetheblack-box:Asurveyonexplainableartificialintelligence(xai)

Reference 1

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Observation 167bacee-779d-41e3-a04d-62517c4c51a0 · outbound

This paper cites Fully automated age-weighted expression classification using real and apparent age.

Bridging the gap in FER: addressing age bias in deep learning Fully automated age-weighted expression classification using real and apparent age

Reference 2

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Bridging the gap in FER: addressing age bias in deep learning Unresolved cited work

Reference 3

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Observation f7bfaffb-69c9-4bfd-ac91-bed2efb71f0e · outbound

This paper cites A review study: The effect of face aging at estimating age and face recognition.

Bridging the gap in FER: addressing age bias in deep learning A review study: The effect of face aging at estimating age and face recognition

Reference 4

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Observation 372d1caa-596c-46f1-9895-69b770f8dae5 · outbound

This paper cites Explainableartificialintelligence(xai):Concepts,taxonomies,opportunitiesandchallengestowardresponsible ai.

Bridging the gap in FER: addressing age bias in deep learning Explainableartificialintelligence(xai):Concepts,taxonomies,opportunitiesandchallengestowardresponsible ai

Reference 5

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Observation e11d78f1-5883-4af3-9e5f-574eb50cfacf · outbound

This paper cites A survey of predictive modeling on imbalanced domains.

Bridging the gap in FER: addressing age bias in deep learning A survey of predictive modeling on imbalanced domains

Reference 6

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Observation 318197f4-d3cc-4b59-ad06-a6252c8cdb54 · outbound

This paper cites Further evidence on preschoolers’ interpretation of facial expressions.

Bridging the gap in FER: addressing age bias in deep learning Further evidence on preschoolers’ interpretation of facial expressions

Reference 7

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Observation 21c3cb87-550f-405d-a68f-a7b5e58eceeb · outbound

This paper cites Comparisonbetweendeeplearningmodelsandtraditionalmachinelearningapproachesforfacial expressionrecognitioninageingadults.

Bridging the gap in FER: addressing age bias in deep learning Comparisonbetweendeeplearningmodelsandtraditionalmachinelearningapproachesforfacial expressionrecognitioninageingadults

Reference 8

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Observation 7cafa2c4-7f70-412b-99dc-2fa4866c60bf · outbound

This paper cites Karolinska directed emotional faces.

Bridging the gap in FER: addressing age bias in deep learning Karolinska directed emotional faces

Reference 9

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This paper cites Thedartmouthdatabaseofchildren’sfaces:Acquisitionandvalidationofanewfacestimulus set.

Bridging the gap in FER: addressing age bias in deep learning Thedartmouthdatabaseofchildren’sfaces:Acquisitionandvalidationofanewfacestimulus set

Reference 10

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Observation 6dca04b9-7b21-45cc-a790-e1f17490fe9f · outbound

This paper cites The Expression of the Emotions in Man and Animals.

Bridging the gap in FER: addressing age bias in deep learning The Expression of the Emotions in Man and Animals

Reference 11

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Observation baa0ca97-dc34-42cf-b699-8f272819dc4d · outbound

This paper cites Demographicbiasinbiometrics:Asurveyonanemergingchallenge.

Bridging the gap in FER: addressing age bias in deep learning Demographicbiasinbiometrics:Asurveyonanemergingchallenge

Reference 12

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Observation 399d1a5f-5dd8-475b-867d-e50125a369ba · outbound

This paper cites Fairness through awareness, in: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, p.

Bridging the gap in FER: addressing age bias in deep learning Fairness through awareness, in: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, p

Reference 13

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This paper cites Faces-a database of facial expressions in young, middle-aged, and older women and men: Development and validation.

Bridging the gap in FER: addressing age bias in deep learning Faces-a database of facial expressions in young, middle-aged, and older women and men: Development and validation

Reference 14

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Observation 28e6fc30-6283-4631-b020-7d31bfb59715 · outbound

This paper cites The nimh child emotional faces picture set (nimh-chefs): A new set of children’s facial emotion stimuli.

Bridging the gap in FER: addressing age bias in deep learning The nimh child emotional faces picture set (nimh-chefs): A new set of children’s facial emotion stimuli

Reference 15

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This paper cites An argument for basic emotions.

Bridging the gap in FER: addressing age bias in deep learning An argument for basic emotions

Reference 16

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Observation 91f8aeb0-8b9c-476d-8fb9-1132333f2bc9 · outbound

This paper cites Facial action coding system : investigator’s guide.

Bridging the gap in FER: addressing age bias in deep learning Facial action coding system : investigator’s guide

Reference 17

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Observation c1e5298b-fbd1-4d0c-a30b-3899a08420a2 · outbound

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Bridging the gap in FER: addressing age bias in deep learning Facialageaffectsemotionalexpressiondecoding

Reference 18

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Observation 11581674-0bd4-4984-88a3-d53c44e1c447 · outbound

This paper cites Deep Learning-Based Facial Expression Recognition for the Elderly: A Systematic Review.

Bridging the gap in FER: addressing age bias in deep learning Deep Learning-Based Facial Expression Recognition for the Elderly: A Systematic Review

Reference 19

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Observation 75dd8e9f-d5fc-4e55-86b4-05bdf6b4e064 · outbound

This paper cites Evaluating Facial Expression Recognition Datasets for Deep Learning: A Benchmark Study with Novel Similarity Metrics.

Bridging the gap in FER: addressing age bias in deep learning Evaluating Facial Expression Recognition Datasets for Deep Learning: A Benchmark Study with Novel Similarity Metrics

Reference 20

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This paper cites Unveiling the human-like similarities of automatic facial expression recognition: An empirical exploration through explainable ai.

Bridging the gap in FER: addressing age bias in deep learning Unveiling the human-like similarities of automatic facial expression recognition: An empirical exploration through explainable ai

Reference 21

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This paper cites Le développement de la reconnaissance des expressions faciales des émotions chez l’enfant.

Bridging the gap in FER: addressing age bias in deep learning Le développement de la reconnaissance des expressions faciales des émotions chez l’enfant

Reference 22

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Observation 0f82f4ab-3971-4830-99da-8419b80c1cba · outbound

This paper cites Children’sknowledgeoffacialexpressionsofemotions:Distinguishingfearandsurprise.

Bridging the gap in FER: addressing age bias in deep learning Children’sknowledgeoffacialexpressionsofemotions:Distinguishingfearandsurprise

Reference 23

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This paper cites Having difficulties reading the facial expression of older individuals? blame it on the facial muscles, not the wrinkles.

Bridging the gap in FER: addressing age bias in deep learning Having difficulties reading the facial expression of older individuals? blame it on the facial muscles, not the wrinkles

Reference 24

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Bridging the gap in FER: addressing age bias in deep learning Xai—explainable artificial intelligence

Reference 25

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Bridging the gap in FER: addressing age bias in deep learning Facial expression recognition influenced by human aging

Reference 26

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This paper cites Human age estimation: What is the influence across race and gender?, in: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, pp.

Bridging the gap in FER: addressing age bias in deep learning Human age estimation: What is the influence across race and gender?, in: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, pp

Reference 27

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This paper cites Deep residual learning for image recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Bridging the gap in FER: addressing age bias in deep learning Deep residual learning for image recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 28

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This paper cites Searchingformobilenetv3,in:2019IEEE/CVFInternationalConferenceonComputerVision(ICCV),pp.1314–1324.

Bridging the gap in FER: addressing age bias in deep learning Searchingformobilenetv3,in:2019IEEE/CVFInternationalConferenceonComputerVision(ICCV),pp.1314–1324

Reference 29

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This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Bridging the gap in FER: addressing age bias in deep learning MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 30

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This paper cites Facial expression recognition with age-group expression feature learning, in: 2024 International Joint Conference on Neural Networks (IJCNN), pp.

Bridging the gap in FER: addressing age bias in deep learning Facial expression recognition with age-group expression feature learning, in: 2024 International Joint Conference on Neural Networks (IJCNN), pp

Reference 31

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This paper cites Bringinganecologicalperspectivetothestudyofagingandrecognitionofemotionalfacialexpressions: Past, current, and future methods.

Bridging the gap in FER: addressing age bias in deep learning Bringinganecologicalperspectivetothestudyofagingandrecognitionofemotionalfacialexpressions: Past, current, and future methods

Reference 32

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Observation 7c56aa0a-034f-4a1c-b39b-996ee9448a48 · outbound

This paper cites Expression recognition across age, in: 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), pp.

Bridging the gap in FER: addressing age bias in deep learning Expression recognition across age, in: 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), pp

Reference 33

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Observation 37a74ca5-73ca-4692-b360-8171018f38af · outbound

This paper cites Ultralytics yolo.

Bridging the gap in FER: addressing age bias in deep learning Ultralytics yolo

Reference 34

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Observation 35c1633b-9a9f-46d4-85b8-6dfd22479f09 · outbound

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Bridging the gap in FER: addressing age bias in deep learning Unresolved cited work

Reference 35

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This paper cites Changes in computer-analyzed facial expressions with age.

Bridging the gap in FER: addressing age bias in deep learning Changes in computer-analyzed facial expressions with age

Reference 36

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Observation b1c3c48d-62c5-4038-a5ad-af8841548e0f · outbound

This paper cites Mivolo:Multi-inputtransformerforageandgenderestimation,in:AnalysisofImages,SocialNetworks and Texts, Springer Nature Switzerland, Cham.

Bridging the gap in FER: addressing age bias in deep learning Mivolo:Multi-inputtransformerforageandgenderestimation,in:AnalysisofImages,SocialNetworks and Texts, Springer Nature Switzerland, Cham

Reference 37

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Observation 82f66f9b-16f0-4659-80a1-da7abe1c017c · outbound

This paper cites Presentation and validation of the radboud faces database.

Bridging the gap in FER: addressing age bias in deep learning Presentation and validation of the radboud faces database

Reference 38

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Observation 6a6fc325-e925-4326-bf92-f3d70cf47a4c · outbound

This paper cites Deep facial expression recognition: A survey.

Bridging the gap in FER: addressing age bias in deep learning Deep facial expression recognition: A survey

Reference 39

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Observation c63d7637-14d1-472d-8eee-ac235d1039fe · outbound

This paper cites Exploring disentangled feature representation beyond face identification, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Bridging the gap in FER: addressing age bias in deep learning Exploring disentangled feature representation beyond face identification, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 40

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Observation b4b3b4bc-73b5-48ed-9ae3-7a071db72dc6 · outbound

This paper cites Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.

Bridging the gap in FER: addressing age bias in deep learning Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Reference 41

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Observation 110c084c-b95a-4607-8725-ee33d2b66475 · outbound

This paper cites A ConvNet for the 2020s.

Bridging the gap in FER: addressing age bias in deep learning A ConvNet for the 2020s

Reference 42

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Observation 8bafa5dc-f061-4311-b38b-c1864cc0d130 · outbound

This paper cites Analysisofgenderdifferencesinfacialexpressionrecognitionbasedondeeplearningusing explainable artificial intelligence.

Bridging the gap in FER: addressing age bias in deep learning Analysisofgenderdifferencesinfacialexpressionrecognitionbasedondeeplearningusing explainable artificial intelligence

Reference 43

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Observation 099d71a2-8129-4a32-9c2c-40ab5ac40006 · outbound

This paper cites A survey on bias and fairness in machine learning.

Bridging the gap in FER: addressing age bias in deep learning A survey on bias and fairness in machine learning

Reference 44

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Observation 4c3f9fee-f466-4a7d-b8d4-5897d1647127 · outbound

This paper cites Thecreationandvalidationofthedevelopmentalemotionalfacesstimulusset.

Bridging the gap in FER: addressing age bias in deep learning Thecreationandvalidationofthedevelopmentalemotionalfacesstimulusset

Reference 45

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verified exact
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Observation 7f8ea42f-8c7f-41c1-877d-3a8a9b7ecf39 · outbound

This paper cites Alifespandatabaseofadultfacialstimuli.

Bridging the gap in FER: addressing age bias in deep learning Alifespandatabaseofadultfacialstimuli

Reference 46

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation f681d8e2-c75a-4741-928c-10475700b1ab · outbound

This paper cites Assessingfidelityinxaipost-hoctechniques:Acomparativestudywithgroundtruth explanations datasets.

Bridging the gap in FER: addressing age bias in deep learning Assessingfidelityinxaipost-hoctechniques:Acomparativestudywithgroundtruth explanations datasets

Reference 47

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Observation dc3b1320-c06e-49b9-9cb5-3a9f2c16f0a6 · outbound

This paper cites Affectnet: A database for facial expression, valence, and arousal computing in the wild.

Bridging the gap in FER: addressing age bias in deep learning Affectnet: A database for facial expression, valence, and arousal computing in the wild

Reference 48

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Observation aa20e276-df27-4d6b-be2e-765afd2a5de8 · outbound

This paper cites Facial expression recognition software | FaceReader.

Bridging the gap in FER: addressing age bias in deep learning Facial expression recognition software | FaceReader

Reference 49

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Observation 2f1747fc-b89d-41f8-8dda-c3c9d119a079 · outbound

This paper cites Warsawsetofemotionalfacialexpression pictures: A validation study of facial display photographs.

Bridging the gap in FER: addressing age bias in deep learning Warsawsetofemotionalfacialexpression pictures: A validation study of facial display photographs

Reference 50

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verified exact
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Observation e6c8adfb-089f-48d7-9227-5839cabe1c21 · outbound

This paper cites Facial emotion recognition analysis based on age-biased data.

Bridging the gap in FER: addressing age bias in deep learning Facial emotion recognition analysis based on age-biased data

Reference 51

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Observation b2d5cb51-ed26-4987-8827-485c5b642c08 · outbound

This paper cites Shape preserving facial landmarks with graph attention networks, in: 33rd British Machine Vision Conference 2022, BMVC 2022, London, UK, November 21-24, 2022, BMVA Press.

Bridging the gap in FER: addressing age bias in deep learning Shape preserving facial landmarks with graph attention networks, in: 33rd British Machine Vision Conference 2022, BMVC 2022, London, UK, November 21-24, 2022, BMVA Press

Reference 52

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Observation 8df485d0-dc1e-4334-adb0-0937e890867d · outbound

This paper cites A novel approach to cross dataset studies in facial expression recognition.

Bridging the gap in FER: addressing age bias in deep learning A novel approach to cross dataset studies in facial expression recognition

Reference 53

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Observation 4efe0797-be35-428d-93d9-46a01e741c8b · outbound

This paper cites Explainablefacialexpressionrecognitionforpeoplewith intellectual disabilities, in: Proceedings of the XXIII International Conference on Human Computer Interaction.

Bridging the gap in FER: addressing age bias in deep learning Explainablefacialexpressionrecognitionforpeoplewith intellectual disabilities, in: Proceedings of the XXIII International Conference on Human Computer Interaction

Reference 54

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Observation 1b1b144b-4623-40f5-a308-751cb363a540 · outbound

This paper cites Whyshoulditrustyou?:Explainingthepredictionsofanyclassifier,in:Proceedingsofthe22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p.

Bridging the gap in FER: addressing age bias in deep learning Whyshoulditrustyou?:Explainingthepredictionsofanyclassifier,in:Proceedingsofthe22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p

Reference 55

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Observation 18e833e8-6b25-405f-9d75-687fd9e25932 · outbound

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Bridging the gap in FER: addressing age bias in deep learning Unresolved cited work

Reference 56

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Observation 92e9d7f7-2b76-4c96-b59f-0331639c5bd7 · outbound

This paper cites A meta-analytic review of emotion recognition and aging: Implications for neuropsychological models of aging.

Bridging the gap in FER: addressing age bias in deep learning A meta-analytic review of emotion recognition and aging: Implications for neuropsychological models of aging

Reference 57

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Observation 8a53e582-c1a8-40ba-bdbc-a3882051a28c · outbound

This paper cites an unresolved cited work.

Bridging the gap in FER: addressing age bias in deep learning Unresolved cited work

Reference 58

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Observation eecef9c8-5b52-44ec-a943-d1f5d8e01bdb · outbound

This paper cites Density-based weighting for imbalanced regression.

Bridging the gap in FER: addressing age bias in deep learning Density-based weighting for imbalanced regression

Reference 59

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Observation 9160c57f-8b16-4914-99b5-e106db3db0fb · outbound

This paper cites an unresolved cited work.

Bridging the gap in FER: addressing age bias in deep learning Unresolved cited work

Reference 60

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Observation 97f36074-4bad-48f2-95c6-f792e0f6556b · outbound

This paper cites A computational study on aging effect for facial expression recognition.

Bridging the gap in FER: addressing age bias in deep learning A computational study on aging effect for facial expression recognition

Reference 61

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Observation c6941ae5-405f-485b-a9fa-6feb096f365d · outbound

This paper cites Enhancedfacialexpressionrecognitionbyage,in:201511thIEEEInternationalConferenceandWorkshops on Automatic Face and Gesture Recognition (FG), pp.

Bridging the gap in FER: addressing age bias in deep learning Enhancedfacialexpressionrecognitionbyage,in:201511thIEEEInternationalConferenceandWorkshops on Automatic Face and Gesture Recognition (FG), pp

Reference 62

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 59ef162d-c0e2-4658-8be1-6ee14166ee3c · outbound

This paper cites Investigating bias and fairness in facial expression recognition, in: Computer Vision – ECCV 2020 Workshops, Springer International Publishing, Cham.

Bridging the gap in FER: addressing age bias in deep learning Investigating bias and fairness in facial expression recognition, in: Computer Vision – ECCV 2020 Workshops, Springer International Publishing, Cham

Reference 63

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Source-reported events for the cited work

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Observation b8374ca0-7f8a-4682-a666-5779bf2da3da · outbound

This paper cites Joint estimation of age and expression by combining scattering and convolutional networks.

Bridging the gap in FER: addressing age bias in deep learning Joint estimation of age and expression by combining scattering and convolutional networks

Reference 64

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 174ac75d-b54a-47d6-a428-213e9924e3ed · outbound

This paper cites Ageprogression/regressionbyconditionaladversarialautoencoder,in:2017IEEEConferenceonComputer Vision and Pattern Recognition (CVPR), pp.

Bridging the gap in FER: addressing age bias in deep learning Ageprogression/regressionbyconditionaladversarialautoencoder,in:2017IEEEConferenceonComputer Vision and Pattern Recognition (CVPR), pp

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source=pdf_text observed=2026-08-06T18:42:47.115780Z digest=sha256:afcf0a88586d1209d5b894a9656cc74e60c8383c92b0e7afba78c5a33dd96f5a

Observation 1e242bfb-d99f-46b2-bcd3-8ba8eb1d9ede · outbound

This paper cites Defining standard values for facereader facial expression software output.

Bridging the gap in FER: addressing age bias in deep learning Defining standard values for facereader facial expression software output

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Pith citing papers

No inbound Pith citation observations are available.