Human perceptual categorization of iris texture patterns

Stark, Louise; Siena, Stephen

doi:10.1109/btas.2010.5634480

Cited by 12 publications

(4 citation statements)

References 11 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study of employing iris on race categorization initiated from [124], which had used Gabor feature and Adaboost selection combination on a combined iris dataset for two-races classification. Their conclusion was that iris is race-related, a result which has been further confirmed by [125], [126], [127], [128], [129], [130], all of which fit well with race recognitions (Asian/Non-Asian, Asian/Black/Cucasian). Specifically, Qiu et al [125] studied the correlation between race and iris texture and concluded that race information is illustrated in iris texture features, with best classification rate of 88.3% by SVM.…”

Section: Iris Texturesupporting

confidence: 71%

Learning Race from Face: A Survey

Hou

2014

IEEE Trans. Pattern Anal. Mach. Intell.

158

View full text Add to dashboard Cite

Faces convey a wealth of social signals, including race, expression, identity, age and gender, all of which have attracted increasing attention from multi-disciplinary research, such as psychology, neuroscience, computer science, to name a few. Gleaned from recent advances in computer vision, computer graphics, and machine learning, computational intelligence based racial face analysis has been particularly popular due to its significant potential and broader impacts in extensive real-world applications, such as security and defense, surveillance, human computer interface (HCI), biometric-based identification, among others. These studies raise an important question: How implicit, non-declarative racial category can be conceptually modeled and quantitatively inferred from the face? Nevertheless, race classification is challenging due to its ambiguity and complexity depending on context and criteria. To address this challenge, recently, significant efforts have been reported toward race detection and categorization in the community. This survey provides a comprehensive and critical review of the state-of-the-art advances in face-race perception, principles, algorithms, and applications. We first discuss race perception problem formulation and motivation, while highlighting the conceptual potentials of racial face processing. Next, taxonomy of feature representational models, algorithms, performance and racial databases are presented with systematic discussions within the unified learning scenario. Finally, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potentially important cross-cutting themes and research directions for the issue of learning race from face.

show abstract

Section: Iris Texturesupporting

confidence: 71%

Learning Race from Face: A Survey

Hou

2014

IEEE Trans. Pattern Anal. Mach. Intell.

158

View full text Add to dashboard Cite

show abstract

“…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%

Deep Learning for Iris Recognition: A Survey

Nguyen¹,

Proença²,

Alonso‐Fernandez³

2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…There are only a few works related to human examination of iris images. Stark et al [18] studied how people classify iris textures into categories. They used a software tool that allowed subjects to browse a set of segmented nearinfrared iris images and use a drag-and-drop scheme to organize the images into groups based on their perception of the iris textures.…”

Section: Related Workmentioning

confidence: 99%

“…The literature of iris recognition has been investigating the performance of humans at tasks such as iris texture perception [18,2,10,11,17] and identity verification [13,9]. Understanding how people perceive and analyze iris fea-tures is useful not only for inspiring the development of better solutions, but also for making them more humanintelligible.…”

Section: Introductionmentioning

confidence: 99%

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

Moreira¹,

Trokielewicz²,

Czajka³

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

This paper advances the state of the art in human examination of iris images by (1) assessing the impact of different iris conditions in identity verification, and (2) introducing an annotation step that improves the accuracy of people's decisions. In a first experimental session, 114 subjects were asked to decide if pairs of iris images depict the same eye (genuine pairs) or two distinct eyes (impostor pairs). The image pairs sampled six conditions: (1) easy for algorithms to classify, (2) difficult for algorithms to classify, (3) large difference in pupil dilation, (4) disease-affected eyes, (5) identical twins, and (6) post-mortem samples. In a second session, 85 of the 114 subjects were asked to annotate matching and non-matching regions that supported their decisions. Subjects were allowed to change their initial classification as a result of the annotation process. Results suggest that: (a) people improve their identity verification accuracy when asked to annotate matching and nonmatching regions between the pair of images, (b) images depicting the same eye with large difference in pupil dilation were the most challenging to subjects, but benefited well from the annotation-driven classification, (c) humans performed better than iris recognition algorithms when verifying genuine pairs of post-mortem and disease-affected eyes (i.e., samples showing deformations that go beyond the distortions of a healthy iris due to pupil dilation), and (d) annotation does not improve accuracy of analyzing images from identical twins, which remain confusing for people.

show abstract

Human perceptual categorization of iris texture patterns

Cited by 12 publications

References 11 publications

Learning Race from Face: A Survey

Learning Race from Face: A Survey

Deep Learning for Iris Recognition: A Survey

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

Contact Info

Product

Resources

About