Performance of Humans in Iris Recognition: The Impact of Iris Condition and Annotation-Driven Verification

Moreira, Daniel; Trokielewicz, Mateusz; Czajka, Adam; Bowyer, Kevin W.; Flynn, Patrick J.

doi:10.1109/wacv.2019.00105

Cited by 14 publications

(6 citation statements)

References 18 publications

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“…This human-machine pairing is important as human subjects can provide an incorrect decision even despite spending quite sometime observing many iris regions [120]. In addition, there has been a body of research showing that humans and machines do not perform similarly well under different conditions [20,108,154]. For example, Moreira et al also showed that machines can outperform humans in healthy easy iris image pairs; however, humans outperform machines in disease-affected iris image pairs [108].…”

Section: Human-machine Pairing To Improve Deep Learning-based Iris Re...mentioning

confidence: 99%

“…In addition, there has been a body of research showing that humans and machines do not perform similarly well under different conditions [20,108,154]. For example, Moreira et al also showed that machines can outperform humans in healthy easy iris image pairs; however, humans outperform machines in disease-affected iris image pairs [108]. Human-machine pairing will improve deep learning based iris recognition.…”

Section: Human-machine Pairing To Improve Deep Learning-based Iris Re...mentioning

confidence: 99%

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Deep Learning for Iris Recognition: A Survey

Nguyen¹,

Proença²,

Alonso‐Fernandez³

2022

Preprint

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In this survey, we provide a comprehensive review of more than 200 papers, technical reports, and GitHub repositories published over the last 10 years on the recent developments of deep learning techniques for iris recognition, covering broad topics on algorithm designs, open-source tools, open challenges, and emerging research. First, we conduct a comprehensive analysis of deep learning techniques developed for two main sub-tasks in iris biometrics: segmentation and recognition. Second, we focus on deep learning techniques for the robustness of iris recognition systems against presentation attacks and via human-machine pairing. Third, we delve deep into deep learning techniques for forensic application, especially in post-mortem iris recognition. Fourth, we review open-source resources and tools in deep learning techniques for iris recognition. Finally, we highlight the technical challenges, emerging research trends, and outlook for the future of deep learning in iris recognition. CCS Concepts: • Security and privacy → Biometrics.

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Section: Human-machine Pairing To Improve Deep Learning-based Iris Re...mentioning

confidence: 99%

Section: Human-machine Pairing To Improve Deep Learning-based Iris Re...mentioning

confidence: 99%

Deep Learning for Iris Recognition: A Survey

Nguyen¹,

Proença²,

Alonso‐Fernandez³

2022

Preprint

View full text Add to dashboard Cite

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“…The segmentation mask is used to set all regions not corresponding to the iris texture to zero (black in the image). Contrast-limited adaptive histogram equalization (CLAHE) is applied to the cropped image to accentuate the iris texture, as reported in [27] to be an effective image enhancement means in case of forensic iris recognition.…”

Section: Data Preprocessingmentioning

confidence: 99%

Human Saliency-Driven Patch-based Matching for Interpretable Post-mortem Iris Recognition

Boyd¹,

Moreira²,

Kuehlkamp³

et al. 2022

Preprint

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Human annotators Forensic examiners Human perception-based training Human interpretable matching CNNbased feature detector Human salience Figure 1: Our proposed human-interpretable forensic iris recognition benefits from using a saliency-driven feature detector trained with regions annotated by examiners solving previous forensic iris comparison tasks. Automatically-detected patches are matched and both the overall comparison score, as well as matching feature pairs, are presented to a human examiner.

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“…RichardWebster et al [44] showed that observing human face recognition behavior in certain contexts can be used to retroactively explain why a face matcher succeeds or fails, leading to better model explainability. In the realm of biometrics, human saliency was found complementary to algorithm saliency and thus beneficial to combine them [38,49]. Czajka et al measured human visual saliency via eye tracking and used it to build human-driven filtering kernels for iris recognition [17], achieving better performance than non-human-driven approaches.…”

Section: Use Of Human Perception To Understand and Improvementioning

confidence: 99%

CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning

Boyd¹,

Tinsley²,

Bowyer³

et al. 2021

Preprint

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Can deep learning models achieve greater generalization if their training is guided by reference to human perceptual abilities? And how can we implement this in a practical manner? This paper proposes a first-ever training strategy to ConveY Brain Oversight to Raise Generalization (CY-BORG). This new training approach incorporates humanannotated saliency maps into a CYBORG loss function that guides the model towards learning features from image regions that humans find salient when solving a given visual task. The Class Activation Mapping (CAM) mechanism is used to probe the model's current saliency in each training batch, juxtapose model saliency with human saliency, and penalize the model for large differences. Results on the task of synthetic face detection show that the CYBORG loss leads to significant improvement in performance on unseen samples consisting of face images generated from six Generative Adversarial Networks (GANs) across multiple classification network architectures. We also show that scaling to even seven times as much training data with standard loss cannot beat the accuracy of CYBORG loss. As a side effect, we observed that the addition of explicit region annotation to the task of synthetic face detection increased human classification performance. This work opens a new area of research on how to incorporate human visual saliency into loss functions. All data, code and pre-trained models used in this work are offered with this paper.

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Performance of Humans in Iris Recognition: The Impact of Iris Condition and Annotation-Driven Verification

Cited by 14 publications

References 18 publications

Deep Learning for Iris Recognition: A Survey

Deep Learning for Iris Recognition: A Survey

Human Saliency-Driven Patch-based Matching for Interpretable Post-mortem Iris Recognition

CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning

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