Pith. sign in

REVIEW 3 major objections 5 minor 2 cited by

Benchmarking Foundation Models for Zero-Shot Biometric Tasks

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A benchmark of 41 vision-language models finds that frozen embeddings verify faces and irises at up to 97% without fine-tuning.

desk verdict A broad and useful zero-shot biometric benchmark that needs a leakage audit before its headline 'zero-shot' claims are fully supported. read the letter →

arxiv 2505.24214 v1 pith:7NC6CQH4 submitted 2025-05-30 cs.CV cs.AI

Redwan Sony , Parisa Farmanifard , Hamzeh Alzwairy , Nitish Shukla , Arun Ross This is my paper · ORCID
classification cs.CVcs.AI
keywords zero-shotbiometricsvision-languagemodelsfaceverificationirisrecognitionpresentationattackdetectiondeepfakemorphfrozenembeddings
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper benchmarks 41 publicly available vision-language and multimodal models on six biometric tasks without any task-specific training, and it finds that several of them already work well enough to be useful. Frozen image encoders, compared by cosine similarity, reach 96.77% true match rate at 1% false match rate on LFW face verification, and 97.55% on IITD-R-Full iris verification. The same frozen features, topped with a simple classifier, reach above 99% accuracy for gender classification and iris presentation attack detection. The paper also shows that deepfake detection is feasible with a small MLP head, while morph attack detection remains close to chance for many models, so the zero-shot success is real but uneven.

What carries the argument

The central object is the frozen vision encoder of each foundation model acting as a generic feature extractor. The paper takes the vision-tower embeddings from models such as CLIP, OpenCLIP, BLIP-2, DINO, DINOv2, and InternVL3, then either compares pairs by cosine similarity for verification or trains a shallow head such as logistic regression, SVM, LDA, KNN, or a two-layer MLP on the frozen features for classification and attack detection. Since no model weights are updated, the benchmark isolates what the pretrained representations alone carry over to biometric tasks.

What would settle it

An audit that checks LFW, IITD iris, FaceForensics++, and VMER images against the pretraining corpora of the top models: if near-duplicates are found and removing them changes the reported TMR@1%FMR by more than a few points, then the zero-shot result is substantially memorization rather than transfer.

Watch

Extended reading notes

Core claim

The central claim is that the image encoders of pretrained VLMs and MLLMs, used without any fine-tuning, already encode discriminative biometric information. For verification, the paper extracts the frozen embeddings of each image and computes cosine similarity between pairs; this yields the strong LFW and IITD-R results. For attribute prediction and attack detection, a lightweight classifier head is trained on the frozen features, producing near-perfect gender and iris PAD accuracies and roughly 90% accuracy for deepfake detection with InternVL3-78B on FaceForensics++. The paper interprets these results as evidence that language-supervised and self-supervised pretraining can substitute for task-specific metric learning in at least some biometric pipelines, with performance depending strongly on model family and task difficulty.

Load-bearing premise

The load-bearing premise is that the benchmark images were not part of the models' pretraining data, so the reported zero-shot scores reflect transferable discrimination rather than memorized identities; the paper does not audit for this overlap.

Editorial extensions

If this is right

  • Face verification can be deployed without biometric-specific training: OpenCLIP-H/14 reaches 96.77% TMR@1%FMR on LFW using only cosine similarity on frozen embeddings.
  • Iris recognition transfers too: DINO-ViT-B/16 reaches 97.55% TMR@1%FMR on IITD-R-Full, with cropped and full iris images performing similarly for many models.
  • Lightweight biometric pipelines are possible: frozen embeddings plus simple classifiers exceed 99% accuracy for gender classification and iris presentation attack detection with the best models.
  • Deepfake detection benefits from large MLLM embeddings, with InternVL3-78B reaching nearly 90% accuracy with a simple MLP head on FaceForensics++.
  • Morph attack detection is the outlier: several models stay at or near chance level, indicating that fine-grained morph artifacts are not captured by current frozen embeddings.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • My inference: the text encoders and chat interfaces of these models are largely unexploited here, so a natural extension is to prompt MLLMs directly with identity and attribute questions and compare those answers against the embedding-only scores.
  • My inference: the large performance gap between IITD and UND iris datasets, and between LFW and CPLFW, points to cross-sensor and cross-pose shifts as the main remaining bottleneck, which could be tested by measuring how frozen-embedding verification degrades on unseen sensors and demographics.
  • My inference: because the pretraining corpora are web-scale, a direct deduplication audit of benchmark images against training data would settle whether the top scores reflect transferable discrimination or memorization, and such an audit is easy to run.
  • My inference: the fact that different model families rank similarly across tasks suggests that pretraining objective and data distribution, rather than parameter count, drive biometric transferability, which a controlled pretraining ablation could confirm.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces a broad benchmark of 41 publicly available vision-language and multimodal models on six biometric tasks in the face and iris domains: face verification, gender and ethnicity classification, iris recognition, iris presentation attack detection (PAD), deepfake detection, and morph attack detection. Using frozen image encoders, the authors compute cosine-similarity verification scores or train lightweight classifiers on top of extracted embeddings. Headline results include 96.77% TMR@1%FMR on LFW with OpenCLIP-H/14, 97.55% on IITD-R-Full with DINO-ViT-B/16, near-99% gender classification with several models, and strong PAD accuracy with DINO/DINOv2 embeddings. The paper also reports negative results, such as chance-level morph detection with Chameleon.

Significance. If the zero-shot interpretation is established, this is a useful reference benchmark for an important question: whether generic foundation-model embeddings carry transferable biometric discriminability. The breadth across 41 models and six tasks is a genuine contribution, and the consistency of the main verification tables makes the raw measurements credible. The paper also gives credit where due: it uses standard public datasets and protocols, reports both strengths and failures across model families, and makes no parameter-free derivation claims that could be circular. The main value is as an empirical baseline; the main risk is that the zero-shot label is not yet supported by a leakage audit.

major comments (3)
  1. [V-D and VII-5] The central 'zero-shot' interpretation is not yet supported because the manuscript never audits whether benchmark images appeared in pretraining corpora. OpenCLIP-H/14 is trained on LAION-5B, DINO and DINOv2 on large web-curated image sets, and LFW, CFP, and AgeDB contain public internet photographs of identifiable individuals; near-duplicate or membership information could plausibly inflate the reported TMR@1%FMR values such as 96.77% on LFW. The paper itself notes 'implicit exposure to ocular patterns during pretraining' in Section V-D and lists understanding the training data as future work in Section VII-5, so the issue is acknowledged but not resolved. Please add a contamination analysis: retrieval-based near-duplicate checks against accessible training corpora, membership or rank statistics, or at minimum a quantitative argument for each high-performing model. Without this, the headline numbers do not distinguish generalization from memorization.
  2. [III-D and Table IV] The zero-shot PAD result ('Acc' column) is not reproducible because no decision rule is specified. Table IV reports binary classification accuracy between Patterned and Normal iris images under 'zero-shot inference,' but the paper never states how a class label is derived from the embeddings: threshold on cosine similarity to class prototypes, nearest-centroid assignment, text-prompt similarity, or another rule. Please specify the protocol, including threshold selection and class priors, or relabel the column so that it does not imply an unsupervised decision rule.
  3. [III-B1, IV-B, and Table II] All attribute-classification results are reported without variance: Section III-B1 states 'Standard deviations are omitted for brevity,' and Section IV-B collapses 5-fold cross-validation to a single mean. Many models differ by less than 0.2% in gender accuracy (values cluster near 99.9%), so the ranking claims in Section V-C are not statistically supported. Please report standard deviations, fold-level extrema, or confidence intervals for Table II.
minor comments (5)
  1. [V-A] The text refers to a '40-year protocol' and assigns the best 30-year result (21.23%) to LLaVA-1.5; Table I has only 5/10/20/30-year columns. Also, the claim that 'BLIP [7] outperformed all others' on CPLFW contradicts Table I, where BLIP-Base scores 7.60 and BLIP-Large 6.03 while BLIP2-t5-xxl scores 43.03.
  2. [V-A] The best CFP-FP result is attributed to 'CLIP-L/14 [28],' but [28] is a prompt-tuning paper and Table I shows the 87.63% value under CLIP-L-32, not a CLIP-L/14 row. Please correct the model name and citation.
  3. [Figure 5] The LLaVA architecture figure cites reference [8] but should cite reference [10]; the caption also contains the typo 'We use only use.'
  4. [Figure 10] The caption contains the typo 'socres' and should read 'scores.'
  5. [IV-C] The morph attack detection setup says a decision tree classifier is trained for each model, but Table VI does not report decision-tree specifics; please state the tree hyperparameters, whether results are averaged over runs, and how BPCER@10%APCER thresholds were selected.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's claims are empirical benchmark measurements with no derivational chain that reduces to fitted values or load-bearing self-citations.

full rationale

The paper is an empirical benchmarking study: face and iris verification scores are computed by cosine similarity between frozen VLM embeddings without any training (Section IV-A, V-A, V-D), so the headline TMR values (e.g., OpenCLIP-H/14's 96.77% on LFW, DINO-ViT-B/16's 97.55% on IITD-R-Full) are direct measurements of off-the-shelf representations, not outputs of a fitted model. The gender/ethnicity, PAD, DeepFake, and morph experiments train lightweight classifiers on extracted embeddings, but they do so under disjoint identity splits and clearly distinguish zero-shot inference from classifier-based evaluation; the classifier results are reported as few-shot/classifier results, not disguised predictions of the frozen encoders. The only self-citations (Iris-SAM, ChatGPT iris analysis, and the two demorphing papers by Shukla and Ross) are used for context or to justify a morph-training strategy, and the strategy itself is fully described in the paper (Section III-F: creating 5,000 training morphs from SMDD with OpenCV/dlib), so no load-bearing claim is imported from an unverified self-citation. The reader's expressed concern about dataset leakage into pretraining corpora is a real external-validity risk for the term 'zero-shot,' but it is not a circularity step: no equation, fitted parameter, or self-citation makes the reported numbers equivalent to the paper's inputs by construction. The central claims are self-contained empirical measurements against fixed public benchmarks, so the circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper is an empirical benchmark. It introduces no new theoretical entities or fitted constants in the verification claims. Its unstated reliance is on the integrity of public checkpoints and datasets, and on the absence of pretraining leakage into the test sets.

assumptions (4)
  • domain assumption Publicly released model checkpoints faithfully represent the published architectures and weights used in the benchmark.
    The paper uses named models without independent verification of checkpoint provenance or exact version hashes.
  • domain assumption Standard verification protocols for LFW, CFP, CPLFW, AgeDB, and iris datasets are applied as originally defined.
    The paper states it follows standard protocols but does not ship the exact pair lists or evaluation scripts.
  • domain assumption Benchmark test images are not present in the pretraining corpora of the evaluated models.
    The 'zero-shot' framing depends on this, yet the paper never audits for dataset leakage from web-scale pretraining data.
  • domain assumption Cosine similarity is a valid fixed metric for comparing embeddings across all model families.
    The paper uses cosine similarity throughout without justifying that it is equally appropriate for every model's feature space.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Benchmarking Foundation Models for Zero-Shot Biometric Tasks." pith.science (2026). https://pith.science/paper/7NC6CQH4

@misc{pith2026250524214,
  author       = {Pith},
  title        = {Pith review of: Benchmarking Foundation Models for Zero-Shot Biometric Tasks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7NC6CQH4}},
  note         = {Machine review of arXiv:2505.24214}
}
read the original abstract

The advent of foundation models, particularly Vision-Language Models (VLMs) and Multi-modal Large Language Models (MLLMs), has redefined the frontiers of artificial intelligence, enabling remarkable generalization across diverse tasks with minimal or no supervision. Yet, their potential in biometric recognition and analysis remains relatively underexplored. In this work, we introduce a comprehensive benchmark that evaluates the zero-shot and few-shot performance of state-of-the-art publicly available VLMs and MLLMs across six biometric tasks spanning the face and iris modalities: face verification, soft biometric attribute prediction (gender and race), iris recognition, presentation attack detection (PAD), and face manipulation detection (morphs and deepfakes). A total of 41 VLMs were used in this evaluation. Experiments show that embeddings from these foundation models can be used for diverse biometric tasks with varying degrees of success. For example, in the case of face verification, a True Match Rate (TMR) of 96.77 percent was obtained at a False Match Rate (FMR) of 1 percent on the Labeled Face in the Wild (LFW) dataset, without any fine-tuning. In the case of iris recognition, the TMR at 1 percent FMR on the IITD-R-Full dataset was 97.55 percent without any fine-tuning. Further, we show that applying a simple classifier head to these embeddings can help perform DeepFake detection for faces, Presentation Attack Detection (PAD) for irides, and extract soft biometric attributes like gender and ethnicity from faces with reasonably high accuracy. This work reiterates the potential of pretrained models in achieving the long-term vision of Artificial General Intelligence.

Figures

Figures reproduced from arXiv: 2505.24214 by the authors.

Figure 1
Figure 1. Illustration of the CLIP model architecture, adapted [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. SAM Architecture from the original paper [3]. In this [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. BLIP2 Architecture from the original paper [8]. We use [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figures from the paper (11 more)
Figure 6
Figure 6. Figure 6: LLaVA Architecture from the original paper [17]. We [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: Example iris images from various benchmark datasets, [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: An example of the identity pairing in FaceForensics++ [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 9
Figure 9. Figure 9: Morphs created from face images IMG1 and IMG2. Webmorph, OpenCV and Facemorph and AMSL are landmark [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 10
Figure 10. Figure 10: Score distributions of the genuine and impostor pairs in the LFW dataset [78] of the top six models for the face [PITH_FULL_IMAGE:figures/full_fig_p010_10.png]
Figure 11
Figure 11. Figure 11: Genuine (left three columns) and impostor (right three columns) pairs from the Labeled Faces in the Wild (LFW) [PITH_FULL_IMAGE:figures/full_fig_p010_11.png]
Figure 12
Figure 12. Figure 12: Comparison of True Match Rate (TMR) at 1% False Match Rate (FMR) for different face recognition (verification) tasks. For each model family, the model with best performance is reported [PITH_FULL_IMAGE:figures/full_fig_p011_12.png]
Figure 13
Figure 13. Figure 13: Comparison of TMR@1%FMR across 10 vision-language model groups on four iris recognition datasets: UND [PITH_FULL_IMAGE:figures/full_fig_p014_13.png]
Figure 14
Figure 14. Figure 14: Accuracy of different VLMs on the IIIT-Delhi-Iris (IITD-P) dataset [86] for Presentation Attack (PA) detection task. [PITH_FULL_IMAGE:figures/full_fig_p014_14.png]
Figure 15
Figure 15. Figure 15: DeepFake detection accuracy by training and cross [PITH_FULL_IMAGE:figures/full_fig_p016_15.png]
Figure 16
Figure 16. Figure 16: Equal Error Rate (EER)(%) and BPCER@10%APCER for Morph Attack Detection for different Vision Language [PITH_FULL_IMAGE:figures/full_fig_p018_16.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DiscoGen: Procedural Generation of Algorithm Discovery Tasks in Machine Learning

    cs.LG 2026-03 conditional novelty 5.0 of 10

    DiscoGen procedurally generates billions of configurable ML algorithm-discovery tasks and a fixed DiscoBench subset so algorithm-discovery agents can be trained and evaluated without contamination or saturation.

  2. Foundation versus Domain-specific Models: Performance Comparison, Fusion, and Explainability in Face Recognition

    cs.CV 2025-07 conditional novelty 5.0 of 10

    On standard face benchmarks, domain-specific face recognition models beat zero-shot foundation models, adding context or fusing scores improves performance at low false-match rates, and GPT-4o can explain and sometime...

Reference graph

Works this paper leans on

107 extracted references · 51 canonical work pages · cited by 2 Pith papers

  1. [1]

    On the Opportunities and Risks of Foundation Models,

    R. Bommasani, D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill et al. , “On the Opportunities and Risks of Foundation Models,” arXiv preprint arXiv:2108.07258, 2021

    arXiv 2021

  2. [2]

    GPT-4 Technical Report,

    J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat et al. , “GPT-4 Technical Report,” arXiv preprint arXiv:2303.08774 , 2023

    arXiv 2023

  3. [3]

    Segment anything,

    A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y . Lo, P. Dollar, and R. Girshick, “Segment anything,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2023, pp. 4015–4026

    2023

  4. [4]

    Segment anything in medical images,

    J. Ma, Y . He, F. Li, L. Han, C. You, and B. Wang, “Segment anything in medical images,” Nature Communications, vol. 15, no. 1, p. 654, 2024. [Online]. Available: https://www.nature.com/articles/ s41467-024-44824-z

    2024

  5. [5]

    Learning transferable visual models from natural language supervision,

    A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al., “Learning transferable visual models from natural language supervision,” in Proceedings of the International Conference on Machine Learning (ICML) , 2021, pp. 8748–8763

    2021

  6. [6]

    Scaling up visual and vision-language representation learning with noisy text supervision,

    C. Jia, Y . Yang, Y . Xia, Y .-T. Chen, Z. Parekh, H. Pham, Q. Le, Y .-H. Sung, Z. Li, and T. Duerig, “Scaling up visual and vision-language representation learning with noisy text supervision,” in International Conference on Machine Learning (ICML) , 2021, pp. 4904–4916

    2021

  7. [7]

    BLIP: Bootstrapping Language- image Pre-training for Unified Vision-language Understanding and Generation,

    J. Li, D. Li, C. Xiong, and S. Hoi, “BLIP: Bootstrapping Language- image Pre-training for Unified Vision-language Understanding and Generation,” in International Conference on Machine Learning (ICML), 2022, pp. 12 888–12 900

    2022

  8. [8]

    BLIP-2: Bootstrapping Language- image Pre-training With Frozen Image Encoders and Large Language Models,

    J. Li, D. Li, S. Savarese, and S. Hoi, “BLIP-2: Bootstrapping Language- image Pre-training With Frozen Image Encoders and Large Language Models,” in International Conference on Machine Learning (ICML) , 2023, pp. 19 730–19 742

    2023

Show all 107 references
  1. [9]

    Flamingo: A visual language model for few-shot learning,

    J.-B. Alayrac, J. Donahue, P. Luc, A. Miech, I. Barr, Y . Hasson, K. Lenc, A. Mensch, K. Millican, M. Reynolds et al. , “Flamingo: A visual language model for few-shot learning,” Advances in Neural Information Processing Systems (NeurIPS), vol. 35, pp. 23 716–23 736, 2022

    2022

  2. [10]

    Improved Baselines With Visual Instruction Tuning,

    H. Liu, C. Li, Y . Li, and Y . J. Lee, “Improved Baselines With Visual Instruction Tuning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2024, pp. 26 296– 26 306. 19

    2024

  3. [11]

    Deepseek-vl: towards real-world vision-language understanding,

    H. Lu, W. Liu, B. Zhang, B. Wang, K. Dong, B. Liu, J. Sun, T. Ren, Z. Li, H. Yang et al., “Deepseek-vl: towards real-world vision-language understanding,” arXiv preprint arXiv:2403.05525 , 2024

    2024 arXiv

  4. [12]

    Microsoft COCO: Common Objects in Context,

    T.-Y . Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll´ar, and C. L. Zitnick, “Microsoft COCO: Common Objects in Context,” in European Conference on Computer Vision (ECCV) , 2014, pp. 740–755

    2014

  5. [13]

    Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering,

    Y . Goyal, T. Khot, D. Summers-Stay, D. Batra, and D. Parikh, “Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 6904–6913

    2017

  6. [14]

    OK-VQA: A Visual Question Answering Benchmark Requiring External Knowl- edge,

    K. Marino, M. Rastegari, A. Farhadi, and H. Hajishirzi, “OK-VQA: A Visual Question Answering Benchmark Requiring External Knowl- edge,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 3195–3204

    2019

  7. [15]

    VizWiz Grand Challenge: Answering Visual Questions from Blind People,

    D. Gurari, Q. Li, A. Stangl, C. Guo, C. Lin, K. Grauman, J. P. Bigham, and J. Luo, “VizWiz Grand Challenge: Answering Visual Questions from Blind People,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 3608– 3617

    2018

  8. [16]

    Glam: Efficient Scaling of Lan- guage Models With Mixture-of-experts,

    N. Du, Y . Huang, A. M. Dai, S. Tong, D. Lepikhin, Y . Xu, M. Krikun, Y . Zhou, A. W. Yu, O. Firat et al. , “Glam: Efficient Scaling of Lan- guage Models With Mixture-of-experts,” in International Conference on Machine Learning (ICML) , 2022, pp. 5547–5569

    2022

  9. [17]

    Deepseek-vl2: Mixture-of-experts vision- language models for advanced multimodal understanding,

    Z. Wu, X. Chen, Z. Pan, X. Liu, W. Liu, D. Dai, H. Gao, Y . Ma, C. Wu, B. Wang et al. , “Deepseek-vl2: Mixture-of-experts vision- language models for advanced multimodal understanding,” arXiv preprint arXiv:2412.10302, 2024

    2024 arXiv

  10. [18]

    Chain-of-thought prompting elicits reasoning in large language models,

    J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. H. Chi, Q. V . Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” in Proceedings of the 36th International Conference on Neural Information Processing Systems (NeurIPS), 2022

    2022

  11. [19]

    Graph of thoughts: Solving elaborate problems with large language models,

    M. Besta, N. Blach, A. Kubicek, R. Gerstenberger, M. Podstawski, L. Gianinazzi, J. Gajda, T. Lehmann, H. Niewiadomski, P. Nyczyk, and T. Hoefler, “Graph of thoughts: Solving elaborate problems with large language models,” Proceedings of the AAAI Conference on Artificial Intell...

    2024 doi

  12. [20]

    Self-consistency improves chain of thought reasoning in language models,

    X. Wang, J. Wei, D. Schuurmans, Q. Le, E. Chi, S. Narang, A. Chowdhery, and D. Zhou, “Self-consistency improves chain of thought reasoning in language models,” arXiv preprint arXiv:2203.11171 , 2023. [Online]. Available: https://arxiv.org/abs/2203.11171

    2023 arXiv

  13. [21]

    Retrieval- augmented Generation for Knowledge-intensive NLP Tasks,

    P. Lewis, E. Perez, A. Piktus, F. Petroni, V . Karpukhin, N. Goyal, H. K ¨uttler, M. Lewis, W.-t. Yih, T. Rockt ¨aschel et al. , “Retrieval- augmented Generation for Knowledge-intensive NLP Tasks,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 33, pp. 9459...

    2020

  14. [22]

    Language Mod- els are Few-shot Learners,

    T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al., “Language Mod- els are Few-shot Learners,” Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 1877–1901, 2020

    1901

  15. [23]

    Multitask prompted training enables zero-shot task generalization,

    V . Sanh, A. Webson, C. Raffel, S. Bach, L. Sutawika, Z. Alyafeai, A. Chaffin, A. Stiegler, A. Raja, M. Dey, M. S. Bari, and Others, “Multitask prompted training enables zero-shot task generalization,” in International Conference on Learning Representations (ICLR) , 2022. [Onl...

    2022

  16. [24]

    Scaling Instruction-finetuned Language Models,

    H. W. Chung, L. Hou, S. Longpre, B. Zoph, Y . Tay, W. Fedus, Y . Li, X. Wang, M. Dehghani, S. Brahmaet al., “Scaling Instruction-finetuned Language Models,” Journal of Machine Learning Research (JMLR) , vol. 25, no. 70, pp. 1–53, 2024

    2024

  17. [25]

    Chameleon: Mixed-modal early-fusion foundation models,

    Chameleon Team, “Chameleon: Mixed-modal early-fusion foundation models,” arXiv preprint arXiv:2405.09818 , 2024. [Online]. Available: https://github.com/facebookresearch/chameleon

    2024 arXiv

  18. [26]

    Training Language Models to Follow Instructions With Human Feedback,

    L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training Language Models to Follow Instructions With Human Feedback,” Advances in Neural Information Processing Systems (NeurIPS), vol. 35, pp. 27 730– 27 744, 2022

    2022

  19. [27]

    Lora: Low-rank Adaptation of Large Language Models

    E. J. Hu, Y . Shen, P. Wallis, Z. Allen-Zhu, Y . Li, S. Wang, L. Wang, W. Chen et al. , “Lora: Low-rank Adaptation of Large Language Models.” ICLR, vol. 1, no. 2, p. 3, 2022

    2022

  20. [28]

    The Power of Scale for Parameter-efficient Prompt Tuning,

    B. Lester, R. Al-Rfou, and N. Constant, “The Power of Scale for Parameter-efficient Prompt Tuning,” arXiv preprint arXiv:2104.08691 , 2021

    2021 arXiv

  21. [29]

    Iris-SAM: Iris segmentation using a foundation model,

    P. Farmanifard and A. Ross, “Iris-SAM: Iris segmentation using a foundation model,” in International Conference on Pattern Recognition and Artificial Intelligence , 2024, pp. 394–409

    2024

  22. [30]

    ChatGPT meets iris biometrics,

    ——, “ChatGPT meets iris biometrics,” in IEEE International Joint Conference on Biometrics (IJCB) , 2024, pp. 1–10

    2024

  23. [31]

    Dinov2: Learning Robust Visual Features Without Supervision,

    M. Oquab, T. Darcet, T. Moutakanni, H. V o, M. Szafraniec, V . Khali- dov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby et al., “Dinov2: Learning Robust Visual Features Without Supervision,” arXiv preprint arXiv:2304.07193, 2023

    2023 arXiv

  24. [32]

    Froundation: Are foundation models ready for face recognition?

    T. Chettaoui, N. Damer, and F. Boutros, “Froundation: Are foundation models ready for face recognition?” Image and Vision Computing, vol. 156, p. 105453, 2025

    2025

  25. [33]

    A comprehensive survey of foundation models in medicine,

    W. Khan, S. Leem, K. B. See, J. K. Wong, S. Zhang, and R. Fang, “A comprehensive survey of foundation models in medicine,” URL https://arxiv. org/abs/2406.10729, 2024

    2024 arXiv

  26. [34]

    Biomedical foundation model: A survey,

    X. Liu, Y . Zhang, Y . Lu, C. Yin, X. Hu, X. Liu, L. Chen, S. Wang, A. Rodriguez, H. Yao et al., “Biomedical foundation model: A survey,” arXiv preprint arXiv:2503.02104 , 2025

    2025

  27. [35]

    Foundational models in medical imaging: A compre- hensive survey and future vision,

    B. Azad, R. Azad, S. Eskandari, A. Bozorgpour, A. Kazerouni, I. Rekik, and D. Merhof, “Foundational models in medical imaging: A compre- hensive survey and future vision,” arXiv preprint arXiv:2310.18689 , 2023

    2023 arXiv

  28. [36]

    A comprehensive survey on pretrained foundation models: A history from BERT to ChatGPT

    C. Zhou, Q. Li, C. Li, J. Yu, Y . Liu, G. Wang, K. Zhang, C. Ji, Q. Yan, L. He et al., “A comprehensive survey on pretrained foundation models: A history from BERT to ChatGPT.” arXiv preprint arXiv:2302.09419, 2023

    2023 arXiv

  29. [37]

    Foundation models defining a new era in vision: a survey and outlook,

    M. Awais, M. Naseer, S. Khan, R. M. Anwer, H. Cholakkal, M. Shah, M.-H. Yang, and F. S. Khan, “Foundation models defining a new era in vision: a survey and outlook,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2025

    2025

  30. [38]

    Vision-language models for vision tasks: A survey,

    J. Zhang, J. Huang, S. Jin, and S. Lu, “Vision-language models for vision tasks: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

    2024

  31. [39]

    Multimodal large language models: A survey,

    J. Wu, W. Gan, Z. Chen, S. Wan, and P. S. Yu, “Multimodal large language models: A survey,” in 2023 IEEE International Conference on Big Data (BigData) . IEEE, 2023, pp. 2247–2256

    2023

  32. [40]

    A survey of multimodel large language models,

    Z. Liang, Y . Xu, Y . Hong, P. Shang, Q. Wang, Q. Fu, and K. Liu, “A survey of multimodel large language models,” in Proceedings of the 3rd International Conference on Computer, Artificial Intelligence and Control Engineering, 2024, pp. 405–409

    2024

  33. [41]

    Exploring the frontier of vision-language models: A survey of current methodologies and future directions,

    A. Ghosh, A. Acharya, S. Saha, V . Jain, and A. Chadha, “Exploring the frontier of vision-language models: A survey of current methodologies and future directions,” arXiv preprint arXiv:2404.07214 , 2024

    2024

  34. [42]

    Unleashing the potential of prompt engineering for large language models,

    B. Chen, Z. Zhang, N. Langren ´e, and S. Zhu, “Unleashing the potential of prompt engineering for large language models,” Patterns, 2025

    2025

  35. [43]

    Facex- former: A unified transformer for facial analysis,

    K. Narayan, V . VS, R. Chellappa, and V . M. Patel, “Facex- former: A unified transformer for facial analysis,” arXiv preprint arXiv:2403.12960, 2024

    2024 arXiv

  36. [44]

    An image is worth 16x16 words: Transformers for image recognition at scale,

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning...

    2021

  37. [45]

    Foundation models and biometrics: A survey and outlook,

    H. O. Shahreza and S. Marcel, “Foundation models and biometrics: A survey and outlook,” TechRxiv, 2025

    2025

  38. [46]

    Exploring ChatGPT for Face Presentation Attack Detection in Zero and Few-Shot in-Context Learning ,

    A. Komaty, H. O. Shahreza, A. George, and S. Marcel, “ Exploring ChatGPT for Face Presentation Attack Detection in Zero and Few-Shot in-Context Learning ,” in IEEE/CVF Winter Conference on Applica- tions of Computer Vision Workshops (WACVW), 2025, pp. 1602–1611

    2025

  39. [47]

    Towards zero-shot differential morphing attack detection with multimodal large language models,

    R. Shekhawat, H. Li, R. Ramachandra, and S. Venkatesh, “Towards zero-shot differential morphing attack detection with multimodal large language models,” arXiv preprint arXiv:2505.15332 , 2025

    2025 arXiv

  40. [48]

    Gemini: A family of highly capable multimodal models,

    G. Team, R. Anil, S. Borgeaud, J.-B. Alayrac, J. Yu, and Others, “Gemini: A family of highly capable multimodal models,” 2025. [Online]. Available: https://arxiv.org/abs/2312.11805

    2025 arXiv

  41. [49]

    Iris recognition with off-the-shelf CNN features: A deep learning perspective,

    K. Nguyen, C. Fookes, A. Ross, and S. Sridharan, “Iris recognition with off-the-shelf CNN features: A deep learning perspective,” IEEE Access, vol. 6, pp. 18 848–18 855, 2017

    2017

  42. [50]

    Grother, M

    P. Grother, M. Ngan, and K. Hanaoka, Face Recognition Vendor Test (FRVT): Part 3, Demographic Effects. National Institute of Standards and Technology Gaithersburg, MD, 2019

    2019

  43. [51]

    Saving face: Investigating the ethical concerns of facial recognition auditing,

    I. D. Raji, T. Gebru, M. Mitchell, J. Buolamwini, J. Lee, and E. Denton, “Saving face: Investigating the ethical concerns of facial recognition auditing,” in Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2020, pp. 145–151. 20

    2020

  44. [52]

    Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification,

    J. Buolamwini and T. Gebru, “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification,” in Proceedings of the 1st Conference on Fairness, Accountability and Transparency , S. A. Friedler and C. Wilson, Eds., vol. 81, 23–24 Feb 2018, pp. 77–91. [On...

    2018

  45. [53]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778

    2016

  46. [54]

    ImageNet Large Scale Visual Recognition Challenge,

    O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision , vol. 115, no. 3, pp. 211– 252, 2015

    2015

  47. [55]

    EfficientNet: Rethinking model scaling for convolutional neural networks,

    M. Tan and Q. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proceedings of the 36th International Conference on Machine Learning (ICML) , ser. Proceedings of Machine Learning Research, K. Chaudhuri and R. Salakhutdinov, Eds., vol. 97, 09–15...

    2019

  48. [56]

    BERT: Pre- training of Deep Bidirectional Transformers for Language Understand- ing,

    J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre- training of Deep Bidirectional Transformers for Language Understand- ing,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technol...

    2019

  49. [57]

    Conceptual Cap- tions: A Cleaned, Hypernymed, Image Alt-text Dataset for Automatic Image Captioning,

    P. Sharma, N. Ding, S. Goodman, and R. Soricut, “Conceptual Cap- tions: A Cleaned, Hypernymed, Image Alt-text Dataset for Automatic Image Captioning,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2018, pp....

    2018

  50. [58]

    Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models,

    B. A. Plummer, L. Wang, C. M. Cervantes, J. C. Caicedo, J. Hocken- maier, and S. Lazebnik, “Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) . IEE...

    2015

  51. [59]

    Reproducible scaling laws for contrastive language-image learning,

    M. Cherti, R. Beaumont, R. Wightman, M. Wortsman, G. Ilharco, C. Gordon, C. Schuhmann, L. Schmidt, and J. Jitsev, “Reproducible scaling laws for contrastive language-image learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition (CVPR),...

    2023

  52. [60]

    LAION- 400M: Open dataset of CLIP-filtered 400 million image-text pairs,

    C. Schuhmann, R. Vencu, R. Beaumont, R. Kaczmarczyk, C. Mullis, A. Katta, T. Coombes, J. Jitsev, and A. Komatsuzaki, “LAION- 400M: Open dataset of CLIP-filtered 400 million image-text pairs,” arXiv preprint arXiv:2111.02114 , 2021. [Online]. Available: https://arxiv.org/abs/2111.02114

    2021 arXiv

  53. [61]

    Hugging Face: The AI Community Building the Future,

    Hugging Face, “Hugging Face: The AI Community Building the Future,” https://huggingface.co, 2024, accessed: 2024-05-19

    2024

  54. [62]

    LAION-5B: An open large- scale dataset for training next generation image-text models,

    C. Schuhmann, R. Beaumont, R. Vencu, C. W. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman, P. Schramowski, S. R. Kundurthy, K. Crowson, L. Schmidt, R. Kaczmarczyk, and J. Jitsev, “LAION-5B: An open large- scale dataset for training next generation...

    2022

  55. [63]

    Emerging properties in self-supervised vision trans- formers,

    M. Caron, H. Touvron, I. Misra, H. J ´egou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision trans- formers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2021, pp. 9650–9660

    2021

  56. [64]

    Vicuna: An open-source chatbot impressing GPT-4 with 90%* ChatGPT quality,

    W.-L. Chiang, Z. Li, Z. Lin, Y . Sheng, Z. Wu, H. Zhang, L. Zheng, S. Zhuang, Y . Zhuang, J. E. Gonzalez, I. Stoica, and E. P. Xing, “Vicuna: An open-source chatbot impressing GPT-4 with 90%* ChatGPT quality,” March 2023. [Online]. Available: https://lmsys.org/blog/2023-03-30-vicuna/

    2023

  57. [65]

    LLaMA: Open and efficient foundation language models,

    H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozi `ere, N. Goyal, E. Hambro, F. Azhar, A. Rodriguez, A. Joulin, E. Grave, and G. Lample, “LLaMA: Open and efficient foundation language models,” arXiv preprint arXiv:2302.13971, 2023

    2023 arXiv

  58. [66]

    Kosmos-2: Grounding multimodal large language models to the world,

    Z. Peng, W. Wang, L. Dong, Y . Hao, S. Huang, S. Ma, and F. Wei, “Kosmos-2: Grounding multimodal large language models to the world,” arXiv preprint arXiv:2306.14824 , 2023

    2023 arXiv

  59. [67]

    InternVL3: exploring advanced training and test- time recipes for open-source multimodal models, (Technical Report),

    J. Zhu, W. Wang, Z. Chen, Z. Liu, S. Ye, L. Gu, Y . Duan, H. Tian, W. Su, J. Shao et al., “InternVL3: exploring advanced training and test- time recipes for open-source multimodal models, (Technical Report),” arXiv preprint arXiv:2504.10479 , 2025

    2025 arXiv

  60. [68]

    GPT-4o System Card,

    OpenAI, “GPT-4o System Card,” https://openai.com/index/ gpt-4o-system-card/, 2024, accessed: 2025-05-11

    2024

  61. [69]

    Claude 3.5 sonnet model card addendum,

    Anthropic, “Claude 3.5 sonnet model card addendum,” https://www. anthropic.com/news/claude-3-5-sonnet, 2024, accessed: 2025-05-11

    2024

  62. [70]

    Gemini 2.5: Our most intelligent AI model,

    Google DeepMind, “Gemini 2.5: Our most intelligent AI model,” https://blog.google/technology/google-deepmind/ gemini-model-thinking-updates-march-2025/, 2025, accessed: 2025- 05-11

    2025

  63. [71]

    Deepseekmoe: Towards ultimate expert spe- cialization in mixture-of-experts language models,

    D. Dai, C. Deng, C. Zhao, R. Xu, H. Gao, D. Chen, J. Li, W. Zeng, X. Yu, Y . Wu et al. , “Deepseekmoe: Towards ultimate expert spe- cialization in mixture-of-experts language models,” arXiv preprint arXiv:2401.06066, 2024

    2024 arXiv

  64. [72]

    AgeDB: The first manually collected, in-the-wild age database,

    S. Moschoglou, A. Papaioannou, C. Sagonas, J. Deng, I. Kotsia, and S. Zafeiriou, “AgeDB: The first manually collected, in-the-wild age database,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , 2017, pp. 51–59

    2017

  65. [73]

    Nearest neighbor pattern classification,

    T. M. Cover and P. E. Hart, “Nearest neighbor pattern classification,” IEEE Transactions on Information Theory , vol. 13, no. 1, pp. 21–27, 1967

    1967

  66. [74]

    The use of multiple measurements in taxonomic prob- lems,

    R. A. Fisher, “The use of multiple measurements in taxonomic prob- lems,” Annals of Eugenics , vol. 7, no. 2, pp. 179–188, 1936

    1936

  67. [75]

    The regression analysis of binary sequences,

    D. R. Cox, “The regression analysis of binary sequences,” Journal of the Royal Statistical Society: Series B (Methodological) , vol. 20, no. 2, pp. 215–242, 1958

    1958

  68. [76]

    Ridge regression: Biased estimation for nonorthogonal problems,

    A. E. Hoerl and R. W. Kennard, “Ridge regression: Biased estimation for nonorthogonal problems,” Technometrics, vol. 12, no. 1, pp. 55–67, 1970

    1970

  69. [77]

    Support-vector networks,

    C. Cortes and V . Vapnik, “Support-vector networks,” Machine Learn- ing, vol. 20, no. 3, pp. 273–297, 1995

    1995

  70. [78]

    Labeled Faces in the Wild: A database for studying face recognition in uncon- strained environments,

    G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeled Faces in the Wild: A database for studying face recognition in uncon- strained environments,” in Workshop on Faces in ‘Real-Life’ Images: Detection, Alignment, and Recognition , 2008

    2008

  71. [79]

    Cross-Pose LFW: A database for studying cross-pose face recognition in unconstrained environments,

    T. Zheng and W. Deng, “Cross-Pose LFW: A database for studying cross-pose face recognition in unconstrained environments,” Beijing University of Posts and Telecommunications, Tech. Rep. 18-01, Febru- ary 2018

    2018

  72. [80]

    Frontal to profile face verification in the wild,

    S. Sengupta, J.-C. Chen, C. Castillo, V . M. Patel, R. Chellappa, and D. W. Jacobs, “Frontal to profile face verification in the wild,” in Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), 2016, pp. 1–9

    2016

  73. [81]

    Benchmarking Deep Network Architectures for Ethnicity Recognition Using a New Large Face Dataset,

    A. Greco, G. Percannella, M. Vento, and V . Vigilante, “Benchmarking Deep Network Architectures for Ethnicity Recognition Using a New Large Face Dataset,” Machine Vision and Applications , vol. 31, pp. 1–13, 2020

    2020

  74. [82]

    VGGFace2: A dataset for recognising faces across pose and age,

    Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, “VGGFace2: A dataset for recognising faces across pose and age,” in 13th IEEE International Conference on Automatic Face & Gesture Recognition , 2018, pp. 67–74

    2018

  75. [83]

    Comparison and Combination of Iris Matchers for Reliable Personal Authentication,

    A. Kumar and A. Passi, “Comparison and Combination of Iris Matchers for Reliable Personal Authentication,” Pattern Recognition , vol. 43, no. 3, pp. 1016–1026, 2010

    2010

  76. [84]

    Image Under- standing for Iris Biometrics: A survey,

    K. W. Bowyer, K. Hollingsworth, and P. J. Flynn, “Image Under- standing for Iris Biometrics: A survey,” Computer Vision and Image Understanding, vol. 110, no. 2, pp. 281–307, 2008

    2008

  77. [85]

    Ghclnet: A generalized hierarchically tuned contact lens detection network,

    A. Singh, V . Mistry, D. Yadav, and A. Nigam, “Ghclnet: A generalized hierarchically tuned contact lens detection network,” in IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA), 2018, pp. 1–8

    2018

  78. [86]

    IIT Delhi Iris Database (Ver- sion 1.0),

    IIT Delhi Biometrics Research Lab, “IIT Delhi Iris Database (Ver- sion 1.0),” http://web.iitd.ac.in/ ∼biometrics/Database Iris.htm, 2008, accessed: 2025-05-23

    2008

  79. [87]

    FaceForensics++: Learning to detect manipulated facial images,

    A. R ¨ossler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Nießner, “FaceForensics++: Learning to detect manipulated facial images,” in International Conference on Computer Vision (ICCV) , 2019

    2019

  80. [88]

    Deepfakes,

    GitHub, “Deepfakes,” https://github.com/deepfakes/faceswap, 2020, ac- cessed: 2025-05-15

    2020

  81. [89]

    Face2Face: real-time face capture and reenactment of RGB videos,

    J. Thies, M. Zollh ¨ofer, M. Stamminger, C. Theobalt, and M. Nießner, “Face2Face: real-time face capture and reenactment of RGB videos,” Communications of ACM , vol. 62, no. 1, p. 96–104, 2018. [Online]. Available: https://doi.org/10.1145/3292039

    2018 doi

  82. [90]

    Faceswap,

    M. GitHub, “Faceswap,” https://github.com/MarekKowalski/ FaceSwap/, 2021, accessed: 2025-05-15

    2021

  83. [91]

    Deferred neural rendering: image synthesis using neural textures,

    J. Thies, M. Zollh ¨ofer, and M. Nießner, “Deferred neural rendering: image synthesis using neural textures,” ACM Transactions of Graphics, vol. 38, no. 4, 2019. 21

    2019

  84. [92]

    Extended StirTrace Benchmarking of Biometric and Forensic Qualities of Morphed Face Images,

    T. Neubert, A. Makrushin, M. Hildebrandt, C. Kraetzer, and J. Dittmann, “Extended StirTrace Benchmarking of Biometric and Forensic Qualities of Morphed Face Images,” IET Biometrics , vol. 7, pp. 325–332, 2018

    2018

  85. [93]

    Face Research Lab London (FRLL) Image Dataset,

    L. DeBruine and B. Jones, “Face Research Lab London (FRLL) Image Dataset,” May 2017, https://figshare.com/articles/dataset/Face Research Lab London Set/5047666/3

    2017

  86. [94]

    MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders,

    N. Damer, M. Fang, P. Siebke, J. N. Kolf, M. Huber, and F. Boutros, “MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders,” in Proceedings of 11th International Workshop on Biometrics and Forensics (IWBF) , 2023...

    2023

  87. [95]

    Face morph using OpenCV — C++ / Python,

    S. Mallick, “Face morph using OpenCV — C++ / Python,” LearnOpenCV, 2016, https://learnopencv.com/ face-morph-using-opencv-cpp-python/

    2016

  88. [96]

    Analyzing and Improving the Image Quality of StyleGAN,

    T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and Improving the Image Quality of StyleGAN,” in Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020

    2020

  89. [97]

    debruine/webmorph morphing software: Beta release 2,

    L. DeBruine, “debruine/webmorph morphing software: Beta release 2,” Jan. 2018, https://doi.org/10.5281/zenodo.1162670

    2018 doi

  90. [98]

    Face morpher,

    A. Quek, “Face morpher,” https://github.com/alyssaq/face-morpher, 2016, accessed: 2025-05-19

    2016

  91. [99]

    dc-GAN: Dual-Conditioned GAN for Face Demorphing From a Single Morph,

    N. Shukla and A. Ross, “dc-GAN: Dual-Conditioned GAN for Face Demorphing From a Single Morph,” in Proceedings of IEEE Interna- tional Conference on Automatic Face and Gesture Recognition , 2025

    2025

  92. [100]

    Metric for Evaluating Performance of Reference-Free Demor- phing Methods,

    ——, “Metric for Evaluating Performance of Reference-Free Demor- phing Methods,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) , 2025, pp. 1585–1591

    2025

  93. [101]

    Privacy-Friendly Synthetic Data for the Development of Face Morphing Attack Detectors,

    N. Damer, C. A. F. L ´opez, M. Fang, N. Spiller, M. V . Pham, and F. Boutros, “Privacy-Friendly Synthetic Data for the Development of Face Morphing Attack Detectors,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 20...

    2022

  94. [102]

    LLaV A-NeXT: Improved reasoning, ocr, and world knowledge,

    H. Liu, C. Li, Y . Li, B. Li, Y . Zhang, S. Shen, and Y . J. Lee, “LLaV A-NeXT: Improved reasoning, ocr, and world knowledge,” January 2024. [Online]. Available: https://llava-vl.github.io/blog/ 2024-01-30-llava-next/

    2024

  95. [103]

    CosFace: Large margin cosine loss for deep face recognition,

    H. Wang, Y . Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou, Z. Li, and W. Liu, “CosFace: Large margin cosine loss for deep face recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 5265–5274

    2018

  96. [104]

    ArcFace: Additive angular margin loss for deep face recognition,

    J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “ArcFace: Additive angular margin loss for deep face recognition,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 4690–4699

    2019

  97. [105]

    AdaFace: Quality adaptive margin for face recognition,

    M. Kim, A. K. Jain, and X. Liu, “AdaFace: Quality adaptive margin for face recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022

    2022

  98. [106]

    MagFace: A universal representation for face recognition and quality assessment,

    Q. Meng, S. Zhao, Z. Huang, and F. Zhou, “MagFace: A universal representation for face recognition and quality assessment,” in Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 14 225–14 234

    2021

  99. [107]

    The birth of self supervised learning: A supervised theory,

    R. Balestriero and Y . LeCun, “The birth of self supervised learning: A supervised theory,” in NeurIPS 2024 Workshop: Self- Supervised Learning - Theory and Practice , 2024. [Online]. Available: https://openreview.net/forum?id=NhY AjAAdQT 22

    2024

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.