On Data-Driven Approaches for Presentation Attack Detection in Iris Recognition Systems

Sharma, Deepika; Selwal, Arvind

doi:10.1007/978-981-15-8297-4_38

Cited by 6 publications

(1 citation statement)

References 16 publications

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“…Attackers are constantly developing new attack methodologies to circumvent PAD systems, so an even more important issue is unseen PAIs (i.e. cross-PAI capabilities) [149]. Great results have been achieved on detecting known attack types (known as closed-set recognition), although cross-database evaluation (training in one database an testing in others) still appears as a difficult challenge due to changes in sensors, acquisition environments, or subjects.…”

Section: Open Research Questions In Iris Padmentioning

confidence: 99%

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: Open Research Questions In Iris Padmentioning

confidence: 99%

Deep Learning for Iris Recognition: A Survey

Nguyen¹,

Proença²,

Alonso‐Fernandez³

2022

Preprint

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A survey on face presentation attack detection mechanisms: hitherto and future perspectives

Sharma

Selwal

2023

Multimedia Systems

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The advances in human face recognition (FR) systems have recorded sublime success for automatic and secured authentication in diverse domains. Although the traditional methods have been overshadowed by face recognition counterpart during this progress, computer vision gains rapid traction, and the modern accomplishments address problems with real-world complexity. However, security threats in FR-based systems are a growing concern that offers a new-fangled track to the research community. In particular, recent past has witnessed ample instances of spoofing attacks where imposter breaches security of the system with an artifact of human face to circumvent the sensor module. Therefore, presentation attack detection (PAD) capabilities are instilled in the system for discriminating genuine and fake traits and anticipation of their impact on the overall behavior of the FR-based systems. To scrutinize exhaustively the current state-of-the-art efforts, provide insights, and identify potential research directions on face PAD mechanisms, this systematic study presents a review of face anti-spoofing techniques that use computational approaches. The study includes advancements in face PAD mechanisms ranging from traditional hardware-based solutions to up-to-date handcrafted features or deep learning-based approaches. We also present an analytical overview of face artifacts, performance protocols, and benchmark face anti-spoofing datasets. In addition, we perform analysis of the twelve recent state-of-the-art (SOTA) face PAD techniques on a common platform using identical dataset (i.e., REPLAY-ATTACK) and performance protocols (i.e., HTER and ACA). Our overall analysis investigates that despite prevalent face PAD mechanisms demonstrate potential performance, there exist some crucial issues that requisite a futuristic attention. Our analysis put forward a number of open issues such as; limited generalization to unknown attacks, inadequacy of face datasets for DL-models, training models with new fakes, efficient DL-enabled face PAD with smaller datasets, and limited discrimination of handcrafted features. Furthermore, the COVID-19 pandemic is an additional challenge to the existing face-based recognition systems, and hence to the PAD methods. Our motive is to present a complete reference of studies in this field and orient researchers to promising directions.

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