Contactless verification is possible with iris biometric identification,which helps prevent infections like COVID-19 from spreading.Biometric systems have grown unsteady and dangerous as a result of spoofing assaults ...Contactless verification is possible with iris biometric identification,which helps prevent infections like COVID-19 from spreading.Biometric systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses,replayed the video,and print attacks.The work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris liveness.Seven assorted feature creation ways are studied in the presented solutions,and these created features are explored for the training of eight distinct machine learning classifiers and ensembles.The predicted iris liveness identification variants are evaluated using recall,F-measure,precision,accuracy,APCER,BPCER,and ACER.Three standard datasets were used in the investigation.The main contribution of our study is achieving a good accuracy of 99.18%with a smaller feature vector.The fragmental coefficients of Haar transformed iris image of size 8∗8 utilizing random forest algorithm showed superior iris liveness detection with reduced featured vector size(64 features).Random forest gave 99.18%accuracy.Additionally,conduct an extensive experiment on cross datasets for detailed analysis.The results of our experiments showthat the iris biometric template is decreased in size tomake the proposed framework suitable for algorithmic verification in real-time environments and settings.展开更多
Iris biometrics is a phenotypic biometric trait that has proven to be agnostic to human natural physiological changes.Research on iris biometrics has progressed tremendously,partly due to publicly available iris datab...Iris biometrics is a phenotypic biometric trait that has proven to be agnostic to human natural physiological changes.Research on iris biometrics has progressed tremendously,partly due to publicly available iris databases.Various databases have been available to researchers that address pressing iris biometric challenges such as constraint,mobile,multispectral,synthetics,long-distance,contact lenses,liveness detection,etc.However,these databases mostly contain subjects of Caucasian and Asian docents with very few Africans.Despite many investigative studies on racial bias in face biometrics,very few studies on iris biometrics have been published,mainly due to the lack of racially diverse large-scale databases containing sufficient iris samples of Africans in the public domain.Furthermore,most of these databases contain a relatively small number of subjects and labelled images.This paper proposes a large-scale African database named Chinese Academy of Sciences Institute of Automation(CASIA)-Iris-Africa that can be used as a complementary database for the iris recognition community to mediate the effect of racial biases on Africans.The database contains 28717 images of 1023 African subjects(2046 iris classes)with age,gender,and ethnicity attributes that can be useful in demographically sensitive studies of Africans.Sets of specific application protocols are incorporated with the database to ensure the database’s variability and scalability.Performance results of some open-source state-of-the-art(SOTA)algorithms on the database are presented,which will serve as baseline performances.The relatively poor performances of the baseline algorithms on the proposed database despite better performance on other databases prove that racial biases exist in these iris recognition algorithms.展开更多
In vivo imaging of human iris vasculature remains a persistent challenge,limiting our understanding of its relationship with ocular disease pathogenesis.Conventional raster scan optical coherence tomography angiograph...In vivo imaging of human iris vasculature remains a persistent challenge,limiting our understanding of its relationship with ocular disease pathogenesis.Conventional raster scan optical coherence tomography angiography(OCTA)suffers from angular-dependent contrast(including blind spots),limited field of view,and prolonged imaging time—challenges that restrict its clinical utility.We introduce a circular interleaving scan OCTA method that overcomes these barriers by enabling 360 deg high-contrast iris angiography with consistent spatiotemporal sampling and optimized motion contrast.The circular scan design enables directionoptimized sampling:we configured circumferential sampling density to approximately twice the radial density,enhancing detection of radially oriented iris vasculature.A Cartesian–polar coordinate transformation was implemented for eye-motion compensation,vessel realignment,and vasculature reconstruction.Compared with raster scan OCTA,our circular scan protocol demonstrates 1.55×higher efficiency in iris vascular imaging,featuring a superior duty cycle(99.95%versus 82.00%)and eliminating redundant data acquisition from rectangular field corners(27.3%of the circular area).This method improves vessel density measurement by 39.0%and vessel count quantification by 25.2%relative to raster scans.By eliminating angular-dependent blind spots,our method significantly enhances vascular quantification reliability,paving the way to a better understanding of ocular diseases and holding promising potential for future clinical applications.展开更多
基金supported by theResearchers Supporting Project No.RSP-2021/14,King Saud University,Riyadh,Saudi Arabia.
文摘Contactless verification is possible with iris biometric identification,which helps prevent infections like COVID-19 from spreading.Biometric systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses,replayed the video,and print attacks.The work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris liveness.Seven assorted feature creation ways are studied in the presented solutions,and these created features are explored for the training of eight distinct machine learning classifiers and ensembles.The predicted iris liveness identification variants are evaluated using recall,F-measure,precision,accuracy,APCER,BPCER,and ACER.Three standard datasets were used in the investigation.The main contribution of our study is achieving a good accuracy of 99.18%with a smaller feature vector.The fragmental coefficients of Haar transformed iris image of size 8∗8 utilizing random forest algorithm showed superior iris liveness detection with reduced featured vector size(64 features).Random forest gave 99.18%accuracy.Additionally,conduct an extensive experiment on cross datasets for detailed analysis.The results of our experiments showthat the iris biometric template is decreased in size tomake the proposed framework suitable for algorithmic verification in real-time environments and settings.
文摘Iris biometrics is a phenotypic biometric trait that has proven to be agnostic to human natural physiological changes.Research on iris biometrics has progressed tremendously,partly due to publicly available iris databases.Various databases have been available to researchers that address pressing iris biometric challenges such as constraint,mobile,multispectral,synthetics,long-distance,contact lenses,liveness detection,etc.However,these databases mostly contain subjects of Caucasian and Asian docents with very few Africans.Despite many investigative studies on racial bias in face biometrics,very few studies on iris biometrics have been published,mainly due to the lack of racially diverse large-scale databases containing sufficient iris samples of Africans in the public domain.Furthermore,most of these databases contain a relatively small number of subjects and labelled images.This paper proposes a large-scale African database named Chinese Academy of Sciences Institute of Automation(CASIA)-Iris-Africa that can be used as a complementary database for the iris recognition community to mediate the effect of racial biases on Africans.The database contains 28717 images of 1023 African subjects(2046 iris classes)with age,gender,and ethnicity attributes that can be useful in demographically sensitive studies of Africans.Sets of specific application protocols are incorporated with the database to ensure the database’s variability and scalability.Performance results of some open-source state-of-the-art(SOTA)algorithms on the database are presented,which will serve as baseline performances.The relatively poor performances of the baseline algorithms on the proposed database despite better performance on other databases prove that racial biases exist in these iris recognition algorithms.
基金supported by the National Key Research and Development Program of China(Grant No.2021YFF0502900)the National Natural Science Foundation of China(Grant Nos.62575066 and 62027824)+3 种基金the Guangdong Basic and Applied Basic Research Foundation(Grant No.2024A1515011344)the Innovation and Entrepreneurship Teams Project of Guangdong Pearl River Talents Program(Grant No.2019ZT08Y105)the Guangdong-Hong Kong-Macao Intelligent Micro-Nano Optoelectronic Technology Joint Laboratory(Grant No.2020B1212030010)the National Institutes of Health/National Eye Institute(NIH/NEI)(Grant Nos.P30EY07551,R01EY022362,and R01EY022362).
文摘In vivo imaging of human iris vasculature remains a persistent challenge,limiting our understanding of its relationship with ocular disease pathogenesis.Conventional raster scan optical coherence tomography angiography(OCTA)suffers from angular-dependent contrast(including blind spots),limited field of view,and prolonged imaging time—challenges that restrict its clinical utility.We introduce a circular interleaving scan OCTA method that overcomes these barriers by enabling 360 deg high-contrast iris angiography with consistent spatiotemporal sampling and optimized motion contrast.The circular scan design enables directionoptimized sampling:we configured circumferential sampling density to approximately twice the radial density,enhancing detection of radially oriented iris vasculature.A Cartesian–polar coordinate transformation was implemented for eye-motion compensation,vessel realignment,and vasculature reconstruction.Compared with raster scan OCTA,our circular scan protocol demonstrates 1.55×higher efficiency in iris vascular imaging,featuring a superior duty cycle(99.95%versus 82.00%)and eliminating redundant data acquisition from rectangular field corners(27.3%of the circular area).This method improves vessel density measurement by 39.0%and vessel count quantification by 25.2%relative to raster scans.By eliminating angular-dependent blind spots,our method significantly enhances vascular quantification reliability,paving the way to a better understanding of ocular diseases and holding promising potential for future clinical applications.
