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

Abstract

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

“…The study by [8] states that combining TSBTC and GLCM can increase detection accuracy. The TSBTC and GLCM are used to form the feature vectors of iris images.…”

A Patch-Based CNN Built on the VGG-16 Architecture for Real-Time Facial Liveness Detection

“…Deep CNN models have been employed in recent facial liveness identification studies because they provide more accurate liveness detection than the previously presented strategies [7,8,14,15]. For facial anti-spoofing detection, Atoum et al suggested a twostream CNN-based method that included a patch-based CNN and a depth-based CNN [25].…”

“…A facial spoofing attack is an attempt by an unauthorized person to circumvent the facial authentication protocol and the facial verification process by employing deception techniques, such as identity forgery [5]. A printed image of an authorized face or a recorded video from a display may provide sufficient unique data to deceive the system [6,7]. As a result, the resiliency of these security systems can be diminished.…”

“…Recently, numerous effective facial anti-spoofing methods have been developed [4][5][6][7][8]. They can be generally divided into fixed feature-based facial anti-spoofing algorithms [12] and automated learnable feature-based facial anti-spoofing algorithms [13].…”

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

“…Despite the iris having a distinct textural structure, the imposter might falsify it [7]. Table 1 shows the iris presentation attacks used that are found in the literature [8].…”