17 research outputs found
Presentation Attack detection using Wavelet Transform and Deep Residual Neural Net
Biometric authentication is becoming more prevalent for secured
authentication systems. However, the biometric substances can be deceived by
the imposters in several ways. Among other imposter attacks, print attacks,
mask attacks, and replay attacks fall under the presentation attack category.
The bio-metric images, especially the iris and face, are vulnerable to
different presentation attacks. This research applies deep learning approaches
to mitigate presentation attacks in a biometric access control system. Our
contribution in this paper is two-fold: First, we applied the wavelet transform
to extract the features from the biometric images. Second, we modified the deep
residual neural net and applied it to the spoof datasets in an attempt to
detect the presentation attacks. This research applied the proposed approach to
biometric spoof datasets, namely ATVS, CASIA two class, and CASIA cropped image
sets. The datasets used in this research contain images that are captured in
both a controlled and uncontrolled environment along with different resolutions
and sizes. We obtained the best accuracy of 93% on the ATVS Iris datasets. For
CASIA two class and CASIA cropped datasets, we achieved test accuracies of 91%
and 82%, respectively
Presentation Attack Detection using Convolutional Neural Networks and Local Binary Patterns
The use of biometrics to authenticate users and control access to secure
areas has become extremely popular in recent years, and biometric access
control systems are frequently used by both governments and private
corporations. However, these systems may represent risks to security when
deployed without considering the possibility of biometric presentation attacks
(also known as spoofing). Presentation attacks are a serious threat because
they do not require significant time, expense, or skill to carry out while
remaining effective against many biometric systems in use today. This research
compares three different software-based methods for facial and iris
presentation attack detection in images. The first method uses Inception-v3, a
pre-trained deep Convolutional Neural Network (CNN) made by Google for the
ImageNet challenge, which is retrained for this problem. The second uses a
shallow CNN based on a modified Spoofnet architecture, which is trained
normally. The third is a texture-based method using Local Binary Patterns
(LBP). The datasets used are the ATVS-FIr dataset, which contains real and fake
iris images, and the CASIA Face Anti-Spoofing Dataset, which contains real
images as well as warped photos, cut photos, and video replay presentation
attacks. We also present a third set of results, based on cropped versions of
the CASIA images
IRDO: Iris Recognition by Fusion of DTCWT and OLBP
Iris Biometric is a physiological trait of human beings. In this paper, we propose Iris an Recognition using Fusion of Dual Tree Complex Wavelet Transform (DTCWT) and Over Lapping Local Binary Pattern (OLBP) Features. An eye is preprocessed to extract the iris part and obtain the Region of Interest (ROI) area from an iris. The complex wavelet features are extracted for region from the Iris DTCWT. OLBP is further applied on ROI to generate features of magnitude coefficients. The resultant features are generated by fusing DTCWT and OLBP using arithmetic addition. The Euclidean Distance (ED) is used to compare test iris with database iris features to identify a person. It is observed that the values of Total Success Rate (TSR) and Equal Error Rate (EER) are better in the case of proposed IRDO compared to the state-of-the art technique
Integration of biometrics and steganography: A comprehensive review
The use of an individual’s biometric characteristics to advance authentication and verification technology beyond the current dependence on passwords has been the subject of extensive research for some time. Since such physical characteristics cannot be hidden from the public eye, the security of digitised biometric data becomes paramount to avoid the risk of substitution or replay attacks. Biometric systems have readily embraced cryptography to encrypt the data extracted from the scanning of anatomical features. Significant amounts of research have also gone into the integration of biometrics with steganography to add a layer to the defence-in-depth security model, and this has the potential to augment both access control parameters and the secure transmission of sensitive biometric data. However, despite these efforts, the amalgamation of biometric and steganographic methods has failed to transition from the research lab into real-world applications. In light of this review of both academic and industry literature, we suggest that future research should focus on identifying an acceptable level steganographic embedding for biometric applications, securing exchange of steganography keys, identifying and address legal implications, and developing industry standards
Um estudo comparativo de contramedidas para detectar ataques de spoofing em sistemas de autenticação de faces
Orientador: José Mario De MartinoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O Resumo poderá ser visualizado no texto completo da tese digitalAbstract: The complete Abstract is available with the full electronic document.MestradoEngenharia de ComputaçãoMestre em Engenharia Elétric