Introduction to Presentation Attack Detection in Iris Biometrics and Recent Advances

• URL: http://arxiv.org/abs/2111.12465v1
• Date: Wed, 24 Nov 2021 12:43:06 GMT
• Title: Introduction to Presentation Attack Detection in Iris Biometrics and Recent Advances
• Authors: Aythami Morales and Julian Fierrez and Javier Galbally and Marta Gomez-Barrero
• Abstract summary: Presentation attacks stand out as some of the most relevant and studied. In the case of the iris, these attacks include the use of real irises as well as artifacts with different level of sophistication such as photographs or videos. This chapter introduces iris Presentation Attack Detection (PAD) methods that have been developed to reduce the risk posed by presentation attacks.
• Score: 13.393219486444604
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Iris recognition technology has attracted an increasing interest in the last decades in which we have witnessed a migration from research laboratories to real world applications. The deployment of this technology raises questions about the main vulnerabilities and security threats related to these systems. Among these threats presentation attacks stand out as some of the most relevant and studied. Presentation attacks can be defined as presentation of human characteristics or artifacts directly to the capture device of a biometric system trying to interfere its normal operation. In the case of the iris, these attacks include the use of real irises as well as artifacts with different level of sophistication such as photographs or videos. This chapter introduces iris Presentation Attack Detection (PAD) methods that have been developed to reduce the risk posed by presentation attacks. First, we summarise the most popular types of attacks including the main challenges to address. Secondly, we present a taxonomy of Presentation Attack Detection methods as a brief introduction to this very active research area. Finally, we discuss the integration of these methods into Iris Recognition Systems according to the most important scenarios of practical application.

Related papers

• Time-Aware Face Anti-Spoofing with Rotation Invariant Local Binary Patterns and Deep Learning [50.79277723970418]
  imitation attacks can lead to erroneous identification and subsequent authentication of attackers. Similar to face recognition, imitation attacks can also be detected with Machine Learning. We propose a novel approach that promises high classification accuracy by combining previously unused features with time-aware deep learning strategies.
  arXiv  Detail & Related papers  (2024-08-27T07:26:10Z)
• A Novel Active Solution for Two-Dimensional Face Presentation Attack Detection [0.0]
  We study state-of-the-art to cover the challenges and solutions related to presentation attack detection. A presentation attack is an attempt to present a non-live face, such as a photo, video, mask, and makeup, to the camera. We introduce an efficient active presentation attack detection approach that overcomes weaknesses in the existing literature.
  arXiv  Detail & Related papers  (2022-12-14T00:30:09Z)
• Face Presentation Attack Detection [59.05779913403134]
  Face recognition technology has been widely used in daily interactive applications such as checking-in and mobile payment. However, its vulnerability to presentation attacks (PAs) limits its reliable use in ultra-secure applicational scenarios.
  arXiv  Detail & Related papers  (2022-12-07T14:51:17Z)
• Physical Adversarial Attack meets Computer Vision: A Decade Survey [55.38113802311365]
  This paper presents a comprehensive overview of physical adversarial attacks. We take the first step to systematically evaluate the performance of physical adversarial attacks. Our proposed evaluation metric, hiPAA, comprises six perspectives.
  arXiv  Detail & Related papers  (2022-09-30T01:59:53Z)
• Presentation Attack Detection Methods based on Gaze Tracking and Pupil Dynamic: A Comprehensive Survey [0.0]
  This research analyzes various aspects of gaze tracking and pupil dynamic presentation attacks. We discuss future work and the open challenges to creating a secure liveness detection based on challenge-based systems.
  arXiv  Detail & Related papers  (2021-12-07T23:22:37Z)
• Introduction to Presentation Attack Detection in Face Biometrics and Recent Advances [21.674697346594158]
  The next pages present the different presentation attacks that a face recognition system can confront. We make an introduction of the current status of face recognition, its level of deployment, and its challenges. We review different types of presentation attack methods, from simpler to more complex ones, and in which cases they could be effective.
  arXiv  Detail & Related papers  (2021-11-23T11:19:22Z)
• Direct attacks using fake images in iris verification [59.68607707427014]
  A database of fake iris images has been created from real iris of the BioSec baseline database. We show that the system is vulnerable to direct attacks, pointing out the importance of having countermeasures.
  arXiv  Detail & Related papers  (2021-10-30T05:01:06Z)
• MixNet for Generalized Face Presentation Attack Detection [63.35297510471997]
  We have proposed a deep learning-based network termed as textitMixNet to detect presentation attacks. The proposed algorithm utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category.
  arXiv  Detail & Related papers  (2020-10-25T23:01:13Z)
• Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain [58.30296637276011]
  This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques. It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
  arXiv  Detail & Related papers  (2020-07-05T18:22:40Z)
• Iris Presentation Attack Detection: Where Are We Now? [14.671313104089906]
  This work presents an overview of the most important advances in the area of iris presentation attack detection. Recent literature can be broken into three categories: traditional "hand-crafted" feature extraction and classification, deep learning-based solutions, and hybrid approaches.
  arXiv  Detail & Related papers  (2020-06-23T18:11:29Z)
• A Survey on Unknown Presentation Attack Detection for Fingerprint [4.424609902825527]
  Biometrics researchers have developed Presentation Attack Detection (PAD) methods as a countermeasure to presentation attacks (PA) PAD is usually done by training a machine learning classifier for known attacks for a given dataset, and they achieve high accuracy. We present a comprehensive survey on existing PAD algorithms for fingerprint recognition systems, specifically from the standpoint of detecting unknown PAD.
  arXiv  Detail & Related papers  (2020-05-17T18:46:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.