Iris Liveness Research Articles

Deep insights on processing strata, features and detectors for fingerprint and iris liveness detection techniques

Deep insights on processing strata, features and detectors for fingerprint and iris liveness detection techniques

Iris Liveness Detection using Fusion of Thepade SBTC and Triangle Thresholding Features with Machine Learning Algorithms

Conventional security systems are often plagued by inherent flaws, leading to frequent security breaches. To address these vulnerabilities, automated biometric systems have emerged, leveraging individuals' physiological and behavioural traits for precise identification. Among these biometric modalities, iris-based authentication is a highly reliable, distinctive, and contactless method for user recognition. This research endeavours to enhance the accuracy of iris liveness detection by combining features extracted from the TSBTC n-Ary (Thepade’s Sorted Block Truncation Coding) method with those derived from the Triangle Thresholding method. Two distinct datasets, namely IIIT Delhi and Clarkson 2015, have been employed to evaluate the efficacy of these combined features. The study involves extracting features from three sources: TSBTC, TSBTC+Triangle, and Triangle methods. These features are subsequently input into the WEKA tool, which employs various classifiers to assess accuracy. The findings of this investigation reveal a notable increase in the accuracy of Iris Liveness Detection (ILD) by incorporating handcrafted techniques like TSBTC in conjunction with the Thresholding method. In essence, this research underscores the potential for improving the robustness of security systems by harnessing the synergy of distinct biometric methods, thereby mitigating the shortcomings of conventional security systems and fortifying the foundations of secure user authentication.

A Fast Iris Liveness Detection for Embedded Systems using Textural Feature Level Fusion Algorithm

Iris recognition is a widely used biometric authentication technique due to its high accuracy and uniqueness. However, iris recognition systems are susceptible to attacks using fake or synthetic iris images, causing a serious security threat. To address this issue, this paper presents a fast iris liveness detection method specifically designed for embedded systems. The proposed method utilizes a textural feature level fusion algorithm using Local Binary Pattern (LBP) and Gray-Level Co-Occurrence Matrix (GLCM) to distinguish between live and printed iris images. LBP captures texture information, while GLCM characterizes the statistical properties of the iris images. By combining these complementary features, the proposed method enhances the discrimination capability and robustness against presentation attacks. Furthermore, to enable real-time and efficient implementation, the proposed liveness detection is optimized and implemented for embedded systems. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method in accurately detecting iris liveness. The proposed fast iris liveness detection is implemented and optimized on C++ which can be complied and deployed in various embedded devices for iris recognition systems on real-world applications, such as access control, biometric authentication, and surveillance systems.

Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers

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 show that the iris biometric template is decreased in size to make the proposed framework suitable for algorithmic verification in real-time environments and settings.

Machine learning-based iris liveness identification using fragmental energy of cosine transformed iris images

Iris biometric identification provides a contactless authentication preventing the spread of COVID-19 like diseases. These systems are made vulnerable and unsafe because of the spoofing attacks attempted with the help of contact lenses, video replays and print attacks. The paper proposes the iris liveness detection method to mitigate spoofing attacks, taking fragmental coefficients of cosine transformed iris image to be used as features. Seven variants of feature formation are considered in experimental validations of the proposed method, and the features are used to train eight assorted machine learning classifiers and ensembles for iris liveness identification. Recall, F-measure, precision and accuracy are used to evaluate performances of the projected iris liveness identification variants. The experimentation carried out on four standard datasets have shown better iris liveness identification by the fragmental coefficients of cosine transformed iris image with size 4 * 4 using random forest algorithm having 99.18% accuracy immediately followed by an ensemble of classifiers.

Iris Liveness Detection Using Multiple Deep Convolution Networks

In the recent decade, comprehensive research has been carried out in terms of promising biometrics modalities regarding humans’ physical features for person recognition. This work focuses on iris characteristics and traits for person identification and iris liveness detection. This study used five pre-trained networks, including VGG-16, Inceptionv3, Resnet50, Densenet121, and EfficientNetB7, to recognize iris liveness using transfer learning techniques. These models are compared using three state-of-the-art biometric databases: the LivDet-Iris 2015 dataset, IIITD contact dataset, and ND Iris3D 2020 dataset. Validation accuracy, loss, precision, recall, and f1-score, APCER (attack presentation classification error rate), NPCER (normal presentation classification error rate), and ACER (average classification error rate) were used to evaluate the performance of all pre-trained models. According to the observational data, these models have a considerable ability to transfer their experience to the field of iris recognition and to recognize the nanostructures within the iris region. Using the ND Iris 3D 2020 dataset, the EfficeintNetB7 model has achieved 99.97% identification accuracy. Experiments show that pre-trained models outperform other current iris biometrics variants.

Identifying discriminatory feature-vectors for fusion-based iris liveness detection

Identifying discriminatory feature-vectors for fusion-based iris liveness detection

Human off Angle IRIS Liveness Detection Based on Fusion of DCT and Zernike Moments

Human off Angle IRIS Liveness Detection Based on Fusion of DCT and Zernike Moments

Iris Liveness Detection Using a Cascade of Dedicated Deep Learning Networks

Iris pattern recognition has significantly improved the biometric authentication field due to its high stability and uniqueness. Such physical characteristics have played an essential role in security applications and other related areas. However, presentation attacks, also known as spoofing techniques, can bypass biometric authentication systems using artefacts such as printed images, artificial eyes, textured contact lenses, etc. Many liveness detection methods that improve the robustness of these systems have been proposed. The first International Iris Liveness Detection competition, where the effectiveness of liveness detection methods is evaluated, was first launched in 2013, and its latest iteration was held in 2020. In this paper, we present the approach that won the LivDet-Iris 2020 competition using two-class scenarios (bona fide iris images vs. presentation attack iris images). Additionally, we propose new three-class and four-class scenarios that complement the competition results. These methods use a serial architecture based on a MobileNetV2 modification, trained from scratch to classify bona fide iris images versus presentation attack images. The bona fide class consists of live iris images, whereas the attack presentation instrument classes consist of cadaver, printed, and contact lenses images, for a total of four species. All the images were pre-processed and weighted per class to present a fair evaluation. This approach is primarily focused on detecting the bona fide class over improving the detection of presentation attack instruments. For the two, three, and four classes scenarios BPCER <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> values of 0.99%, 0.16%, and 0.83% were obtained respectively, whereas for the BPCER <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">20</sub> values of 3.09%, 0.16%, and 3.77% were obtained, with the best model overall being the proposed 3-class serial model. This work reaches competitive results according to the reported results in the LivDet-Iris 2020 competition.

Detection of Iris Presentation Attacks Using Feature Fusion of Thepade's Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features.

Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade’s sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade’s SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade’s SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human–computer interaction and security in the cyber-physical space by improving person validation.

Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions

Biometrics is progressively becoming vital due to vulnerabilities of traditional security systems leading to frequent security breaches. Biometrics is an automated device that studies human beings’ physiological and behavioral features for their unique classification. Iris-based authentication offers stronger, unique, and contactless identification of the user. Iris liveness detection (ILD) confronts challenges such as spoofing attacks with contact lenses, replayed video, and print attacks, etc. Many researchers focus on ILD to guard the biometric system from attack. Hence, it is vital to study the prevailing research explicitly associated with the ILD to address how developing technologies can offer resolutions to lessen the evolving threats. An exhaustive survey of papers on the biometric ILD was performed by searching the most applicable digital libraries. Papers were filtered based on the predefined inclusion and exclusion criteria. Thematic analysis was performed for scrutinizing the data extracted from the selected papers. The exhaustive review now outlines the different feature extraction techniques, classifiers, datasets and presents their critical evaluation. Importantly, the study also discusses the projects, research works for detecting the iris spoofing attacks. The work then realizes in the discovery of the research gaps and challenges in the field of ILD. Many works were restricted to handcrafted methods of feature extraction, which are confronted with bigger feature sizes. The study discloses that dep learning based automated ILD techniques shows higher potential than machine learning techniques. Acquiring an ILD dataset that addresses all the common Iris spoofing attacks is also a need of the time. The survey, thus, opens practical challenges in the field of ILD from data collection to liveness detection and encourage future research.

Using residual images with BSIF for iris liveness detection

Deploying iris recognition systems in several security areas emphasized the importance of developing iris liveness methods. These methods verify if the iris sample acquired for authentication is fake or real. Recently, Binarized Statistical Image Features (BSIF) descriptor was successfully applied for that purpose. As BSIF is a powerful descriptor based on Local Binary Pattern (LBP) descriptor, we have supposed that enhancements that have worked before with LBP could work with BSIF as well. Widening a previous work, four public datasets representing printed, plastic, synthetic, and contact lens attacks were evaluated using 8-bit BSIF in both modes segmented and unsegmented eye images. Contact lens attack was the most challenging attack, especially in the unsegmented scenario. In this paper, a new method is proposed using residual images with BSIF to enhance the results of contact lens databases. Three high pass filters were applied separately before the feature extraction phase to improve the discrimination ability of BSIF. Clarkson contact lens database was used for evaluation in both modes segmented and unsegmented eye images. The results were promising in the unsegmented scenario and the three filters enhanced in the results with 8.6667%, 10%, and 18.3333%. The application of these filters with BSIF could be useful for other computer vision tasks like face liveness detection.

Presentation attack detection system for fake Iris: a review

The real-time solicitations of biometric systems have been extensively used for several things with the growing necessities of higher security level. There are numerous biometric traits used for person identification. In recent years, iris biometric trait become very popular and efficient in many security applications. However, biometric systems are prone to presentation attack. This attack is carried out by using spoofing of any biometric modality and present as a real trait. The objective of this paper is to present a broad and well thought-out overview of the effort that has been conceded out over the preceding years in the field of iris anti-spoofing. Spoofing of an iris image can be done by paper print and contact lens etc. So, it is essential to discover fake iris up to its core level. In this survey, we have discussed various research work done in the past on the topic of different classes of presentation attack and summarized the state-of-the-art methods in a structural way. We have also discussed the future direction in the field of iris liveness detection.

A New Approach for Real-time Iris Liveness Detection Based on Eye Movement’s Features

This research introduces a new approach for iris liveness detection based on eye movement’s features. Given 50 valid eye frames from real-time video stream, this research identify them as the images that were taken whether directly from humans’ eyes or not. Our method ensures the selected images for further processing are eye ones without user’s cooperation during the sampling process. In addition, it also controls the application timeout by identifying either fake iris or low quality eye images. In my study, this Research used two features to model eye movements: (1) the width/height ratio of the eye outline and (2) the distance from the pupil center to the eye centroid. In order to model the acquired data distribution, this research used a bag of two-component Gaussian Mixing Models (GMMs). The class label of the acquired data depends on the GMMs which it belongs. The average accuracy of my proposed method on my and IriTech, Inc.’s databases was 94.704% and 100%, respectively. This result indicated that my method can make real-time iris-based systems work automatically with high security.

Enhanced Binary Hexagonal Extrema Pattern (EBHXEP) Descriptor for Iris Liveness Detection.

Biometric traits are frequently used by security agencies for automatic recognition of a person. There are numerous biometric traits used for person identification. In recent years, iris biometric trait becomes very popular and efficient in many security applications. However, biometric systems are prone to presentation attack. This attack is carried out by using spoofing of any biometric modality and present as a genuine trait. The effect of an artificial artifact of a humanoid iris could be in the form of contact lens attack and print attack make difficult the expected policy of a biometric liveness system. In this paper, the different and enhanced feature descriptor has been proposed i.e. Enhanced Binary Hexagonal Extrema Pattern (EBHXEP) for forged iris detection. The relationship between the center pixel and its hexa neighbor has been explored by the suggested descriptor. The Proposed approach is tested on ATVS-FIr DB and IIIT-D CLI database for iris liveness detection and the results show better results for liveness detection in term of accuracy and average error rate.

Iris Liveness Detection Using Fusion of Domain-Specific Multiple BSIF and DenseNet Features.

In the past few years, some fusion-based approaches have been proposed to constitute discriminatory features for iris liveness detection. However, several methods exist in the literature for iris feature extraction and, thus, identifying an optimal composite of such features is still a vital challenge. This article also proposes a score-level fusion of two distinct domain-specific features, i.e., multiple binarized statistical image feature (BSIF) and DenseNet-based features. However, instead of randomly scrutinizing such features, statistical tests are executed on six predominant iris features to identify the optimal feature set to combine. Particularly, this work emphasizes textured-lens-based presentation attacks and aims to identify the type of contact lenses within the iris samples. The experimental analysis depicts that the domain-specific features substantially outperform the generic features while discriminating live iris from the artifacts. Furthermore, the proposed fusion-based approach is assessed on three iris datasets and the outcomes are compared with various state of the arts using three validation protocols in terms of equal error rate (EER). The comparative analysis perceived that the proposed method obtains a significant performance gain over the existing approaches and offers an improved benchmark for both, iris liveness detection and contact lens identification.

Composition of Methods to Ensure Iris Liveness and Authenticity

Composition of Methods to Ensure Iris Liveness and Authenticity

Local binary hexagonal extrema pattern (LBHXEP): a new feature descriptor for fake iris detection

Security agencies frequently use biometric traits for automatic recognition of a person. The human iris is the most hopeful biometric authentication that can accurately identify a person from their exclusive features. However, in recent years, different types of spoofing attacks are used to violate the security of a biometric system. Biometrics liveness detection system used to recognize persons in a fast and trustworthy way through the use of unique biological distinctiveness. Presentation of a manufactured article of a human iris in the form of photo attack and contact lens attack could hamper the projected policy of a biometric system. The quality of real and fake iris images shows different textural characteristics. In this paper, we have proposed a novel and proficient feature descriptor, i.e., local binary hexagonal extrema pattern for fake iris detection. The proposed descriptor exploits the relationship between the center pixel and its Hexa neighbor. Hexagonal shape using “six-neighbor approach” is preferable to the rectangular structure due to its higher symmetry, consistent connectivity, and efficient use of space. The proposed consideration also solves the “curse of dimensionality” problem in liveness detection. The proposed descriptor is evaluated on ATVS-FIr DB and IIIT-D CLI databases for iris liveness detection and show promising performance for liveness detection in terms green, brown, etc. of accuracy and average error rate.

Enhanced gray-level image space for iris liveness detection

Enhanced gray-level image space for iris liveness detection

Detecting Iris Liveness with Batch Normalized Convolutional Neural Network

Aim to countermeasure the presentation attack for iris recognition system, an iris liveness detection scheme based on batch normalized convolutional neural network (BNCNN) is proposed to improve the reliability of the iris authentication system. The BNCNN architecture with eighteen layers is constructed to detect the genuine iris and fake iris, including convolutional layer, batch-normalized (BN) layer, Relu layer, pooling layer and full connected layer. The iris image is first preprocessed by iris segmentation and is normalized to 256×256 pixels, and then the iris features are extracted by BNCNN. With these features, the genuine iris and fake iris are determined by the decision-making layer. Batch normalization technique is used in BNCNN to avoid the problem of over fitting and gradient disappearing during training. Extensive experiments are conducted on three classical databases: the CASIA Iris Lamp database, the CASIA Iris Syn database and Ndcontact database. The results show that the proposed method can effectively extract micro texture features of the iris, and achieve higher detection accuracy compared with some typical iris liveness detection methods.