mlsanityData to 7 Oct 2026

Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation

arXiv 2210.13129 ↗First posted 24 Oct 2022cs.CVcited by 19

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Abstract (arXiv)

The role of soft biometrics to enhance person recognition systems in unconstrained scenarios has not been extensively studied. Here, we explore the utility of the following modalities: gender, ethnicity, age, glasses, beard, and moustache. We consider two assumptions: 1) manual estimation of soft biometrics and 2) automatic estimation from two commercial off-the-shelf systems (COTS). All experiments are reported using the labeled faces in the wild (LFW) database. First, we study the discrimination capabilities of soft biometrics standalone. Then, experiments are carried out fusing soft biometrics with two state-of-the-art face recognition systems based on deep learning. We observe that soft biometrics is a valuable complement to the face modality in unconstrained scenarios, with relative improvements up to 40%/15% in the verification performance when using manual/automatic soft biometrics estimation. Results are reproducible as we make public our manual annotations and COTS outputs of soft biometrics over LFW, as well as the face recognition scores.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 5 of its 43 references that are in the map. First posted on arXiv 24 Oct 2022.

Builds on

5 of 43 references.

References · 5

  1. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  2. 2017Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approachcited by 43
  3. 2016MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributescited by 34
  4. 2021Quality Measures in Biometric Systemscited by 12
  5. 2016Convolutional Neural Networks for Attribute-based Active Authentication on Mobile Devicescited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 10; 2024: 2; 2025: 4; 2026: 2.

19 papers.

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  1. 2023Towards General Visual-Linguistic Face Forgery Detectioncited by 45
  2. 2024How Good is ChatGPT at Face Biometrics? A First Look into Recognition, Soft Biometrics, and Explainabilitycited by 33
  3. 2023Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitmentcited by 32
  4. 2023Measuring Bias in AI Models: An Statistical Approach Introducing N-Sigmacited by 11
  5. 2022Periocular Biometrics: A Modality for Unconstrained Scenarioscited by 9
  6. 2023Toward Face Biometric De-identification using Adversarial Examplescited by 8
  7. 2023OTB-morph: One-Time Biometrics via Morphingcited by 5
  8. 2023Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluationcited by 5
  9. 2025Exploiting Multiple Representations: 3D Face Biometrics Fusion with Application to Surveillancecited by 4
  10. 2023Turn Fake into Real: Adversarial Head Turn Attacks Against Deepfake Detectioncited by 3
  11. 2023Improving Face Recognition from Caption Supervision with Multi-Granular Contextual Feature Aggregationcited by 2
  12. 2024Massively Annotated Datasets for Assessment of Synthetic and Real Data in Face Recognitioncited by 2
7 more
  1. 2025Balancing Tails when Comparing Distributions: Comprehensive Equity Index (CEI) with Application to Bias Evaluation in Operational Face Biometricscited by 2
  2. 2023Image Generation and Learning Strategy for Deep Document Forgery Detectioncited by 1
  3. 2023Text-Guided Face Recognition using Multi-Granularity Cross-Modal Contrastive Learningcited by 1
  4. 2025Towards General Visual-Linguistic Face Forgery Detection(V2)cited by 0
  5. 2025Towards Robust Protective Perturbation against DeepFake Face Swappingcited by 0
  6. 2026Weight and Height Estimation from a Single Human Image Captured in the Wildcited by 0
  7. 2026VideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Runningcited by 0