Deep Learning in the Field of Biometric Template Protection: An Overview
- URL: http://arxiv.org/abs/2303.02715v1
- Date: Sun, 5 Mar 2023 17:06:40 GMT
- Title: Deep Learning in the Field of Biometric Template Protection: An Overview
- Authors: Christian Rathgeb, Jascha Kolberg, Andreas Uhl, Christoph Busch
- Abstract summary: Deep learning has revolutionised the field of pattern recognition, including biometric recognition.
The interrelation between improved biometric performance rates and security in biometric template protection is elaborated.
The use of deep learning for obtaining feature representations that are suitable for biometric template protection is discussed.
- Score: 18.016337076888924
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Today, deep learning represents the most popular and successful form of
machine learning. Deep learning has revolutionised the field of pattern
recognition, including biometric recognition. Biometric systems utilising deep
learning have been shown to achieve auspicious recognition accuracy, surpassing
human performance. Apart from said breakthrough advances in terms of biometric
performance, the use of deep learning was reported to impact different
covariates of biometrics such as algorithmic fairness, vulnerability to
attacks, or template protection. Technologies of biometric template protection
are designed to enable a secure and privacy-preserving deployment of
biometrics. In the recent past, deep learning techniques have been frequently
applied in biometric template protection systems for various purposes. This
work provides an overview of how advances in deep learning take influence on
the field of biometric template protection. The interrelation between improved
biometric performance rates and security in biometric template protection is
elaborated. Further, the use of deep learning for obtaining feature
representations that are suitable for biometric template protection is
discussed. Novel methods that apply deep learning to achieve various goals of
biometric template protection are surveyed along with deep learning-based
attacks.
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 secure and private ensemble matcher using multi-vault obfuscated templates [1.3518297878940662]
Generative AI has revolutionized modern machine learning by providing unprecedented realism, diversity, and efficiency in data generation.
Biometric template security and secure matching are among the most sought-after features of modern biometric systems.
This paper proposes a novel obfuscation method using Generative AI to enhance biometric template security.
arXiv Detail & Related papers (2024-04-08T05:18:39Z) - AttackNet: Enhancing Biometric Security via Tailored Convolutional Neural Network Architectures for Liveness Detection [20.821562115822182]
AttackNet is a bespoke Convolutional Neural Network architecture designed to combat spoofing threats in biometric systems.
It offers a layered defense mechanism, seamlessly transitioning from low-level feature extraction to high-level pattern discernment.
Benchmarking our model across diverse datasets affirms its prowess, showcasing superior performance metrics in comparison to contemporary models.
arXiv Detail & Related papers (2024-02-06T07:22:50Z) - 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) - Privacy-preserving Multi-biometric Indexing based on Frequent Binary
Patterns [7.092869001331781]
We propose an efficient privacy-preserving multi-biometric identification system that retrieves protected deep cancelable templates.
A multi-biometric binning scheme is designed to exploit the low intra-class variation properties contained in the frequent binary patterns extracted from different types of biometric characteristics.
arXiv Detail & Related papers (2023-10-04T18:18:24Z) - Multi-Channel Time-Series Person and Soft-Biometric Identification [65.83256210066787]
This work investigates person and soft-biometrics identification from recordings of humans performing different activities using deep architectures.
We evaluate the method on four datasets of multi-channel time-series human activity recognition (HAR)
Soft-biometric based attribute representation shows promising results and emphasis the necessity of larger datasets.
arXiv Detail & Related papers (2023-04-04T07:24:51Z) - 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) - 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) - Evaluation of a User Authentication Schema Using Behavioral Biometrics
and Machine Learning [0.0]
This study contributes to the research being done on behavioral biometrics by creating and evaluating a user authentication scheme using behavioral biometrics.
The behavioral biometrics used in this study include touch dynamics and phone movement.
We evaluate the performance of different single-modal and multi-modal combinations of the two biometrics.
arXiv Detail & Related papers (2022-05-07T05:16:34Z) - Semantics-Preserved Distortion for Personal Privacy Protection in Information Management [65.08939490413037]
This paper suggests a linguistically-grounded approach to distort texts while maintaining semantic integrity.
We present two distinct frameworks for semantic-preserving distortion: a generative approach and a substitutive approach.
We also explore privacy protection in a specific medical information management scenario, showing our method effectively limits sensitive data memorization.
arXiv Detail & Related papers (2022-01-04T04:01:05Z) - OTB-morph: One-Time Biometrics via Morphing applied to Face Templates [8.623680649444212]
This paper introduces a new scheme for cancelable biometrics aimed at protecting the templates against potential attacks.
An experimental implementation of the proposed scheme is given for face biometrics.
arXiv Detail & Related papers (2021-11-25T18:35:34Z)
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.