Presentation Attack Detection Methods based on Gaze Tracking and Pupil Dynamic: A Comprehensive Survey

• URL: http://arxiv.org/abs/2112.04038v1
• Date: Tue, 7 Dec 2021 23:22:37 GMT
• Title: Presentation Attack Detection Methods based on Gaze Tracking and Pupil Dynamic: A Comprehensive Survey
• Authors: Jalil Nourmohammadi Khiarak
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Purpose of the research: In the biometric community, visible human characteristics are popular and viable for verification and identification on mobile devices. However, imposters are able to spoof such characteristics by creating fake and artificial biometrics to fool the system. Visible biometric systems have suffered a high-security risk of presentation attack. Methods: In the meantime, challenge-based methods, in particular, gaze tracking and pupil dynamic appear to be more secure methods than others for contactless biometric systems. We review the existing work that explores gaze tracking and pupil dynamic liveness detection. The principal results: This research analyzes various aspects of gaze tracking and pupil dynamic presentation attacks, such as state-of-the-art liveness detection algorithms, various kinds of artifacts, the accessibility of public databases, and a summary of standardization in this area. In addition, we discuss future work and the open challenges to creating a secure liveness detection based on challenge-based systems.

Related papers

• A Critical Analysis on Machine Learning Techniques for Video-based Human Activity Recognition of Surveillance Systems: A Review [1.3693860189056777]
  Upsurging abnormal activities in crowded locations urges the necessity for an intelligent surveillance system. Video-based human activity recognition has intrigued many researchers with its pressing issues. This paper provides a critical survey of video-based Human Activity Recognition (HAR) techniques.
  arXiv  Detail & Related papers  (2024-09-01T14:43:57Z)
• Cross-Database Liveness Detection: Insights from Comparative Biometric Analysis [20.821562115822182]
  Liveness detection is the capability to differentiate between genuine and spoofed biometric samples. This research presents a comprehensive evaluation of liveness detection models. Our work offers a blueprint for navigating the evolving rhythms of biometric security.
  arXiv  Detail & Related papers  (2024-01-29T15:32:18Z)
• GazeForensics: DeepFake Detection via Gaze-guided Spatial Inconsistency Learning [63.547321642941974]
  We introduce GazeForensics, an innovative DeepFake detection method that utilizes gaze representation obtained from a 3D gaze estimation model. Experiment results reveal that our proposed GazeForensics outperforms the current state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-11-13T04:48:33Z)
• Multimodal Adaptive Fusion of Face and Gait Features using Keyless attention based Deep Neural Networks for Human Identification [67.64124512185087]
  Soft biometrics such as gait are widely used with face in surveillance tasks like person recognition and re-identification. We propose a novel adaptive multi-biometric fusion strategy for the dynamic incorporation of gait and face biometric cues by leveraging keyless attention deep neural networks.
  arXiv  Detail & Related papers  (2023-03-24T05:28:35Z)
• Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring [0.0]
  This work focuses on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. In the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed.
  arXiv  Detail & Related papers  (2023-01-08T14:07:01Z)
• A Survey on Computer Vision based Human Analysis in the COVID-19 Era [58.79053747159797]
  The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication
  arXiv  Detail & Related papers  (2022-11-07T17:20:39Z)
• InvisibiliTee: Angle-agnostic Cloaking from Person-Tracking Systems with a Tee [46.611395940966915]
  We propose a black-box adversarial attack method on state-of-the-art human detection models called InvisibiliTee. The method learns printable adversarial patterns for T-shirts that cloak wearers in the physical world in front of person-tracking systems.
  arXiv  Detail & Related papers  (2022-08-15T01:32:09Z)
• No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems [95.54151664013011]
  We present a novel framework to generate adversarial spoofing signals that violate physical properties of the system. We analyze four anomaly detectors published at top security conferences.
  arXiv  Detail & Related papers  (2020-12-07T11:02:44Z)
• Vision-based techniques for gait recognition [6.817301687702869]
  We discuss current techniques of gait recognition and modelling with the environment in which the research was conducted. We provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance.
  arXiv  Detail & Related papers  (2020-04-30T06:33:01Z)
• Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities [52.59080024266596]
  We present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition. We first introduce the multi-modality of the sensory data and provide information for public datasets. We then propose a new taxonomy to structure the deep methods by challenges.
  arXiv  Detail & Related papers  (2020-01-21T09:55:59Z)

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.