Automatic human access to a system has become relatively simple in the digital era. Confirmation of the user’s identification is critical for automated system access. Biometric authentication systems employ biometric features to confirm the identification of a user [1]. Compared to conventional password-based traditional verification systems, the biometric system has a distinct advantage. It minimizes the need to memorize a passcode, pin, or keep a card in hand [2]. Biometric authentication can be thought of as an additional layer of authentication for security-critical cyber applications and existing traditional authentication procedures. Today, there are indeed different businesses for biometric systems. The majority of the sectors seem to be rising quickly. According to www.statista.com (accessed on 24^th June 2022), the business for contactless biometric technologies is forecasted to increase by roughly 30.15 billion US dollars by 2027, while the overall biometric sciences industry is forecasted to touch 19.08 billion US dollars in 2021 [3]. Iris is widely employed in the verification and validation of people. In most applications, it uses, because of its complex textures [4] and distinctive features, such as the UIDAI project for citizen identification in India, the Amsterdam airport, and the Canada-US border onon-US [5].

In comparison to fingerprint and face authentication, iris authentication delivers an additional steadfast contactless user verification. The contactless method aids in the prevention of diseases like COVID-19 [6]. 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].

Individuals frequently assault the biometric system to get admittance to another person’s credentials or to conceal their accurate individuality. The iris identification system can be readily fooled by means of alternative contact lenses (which can be transparent, textured, colored) [10], replaying the video, or using a print attack [9]. As a result, understanding the risk and susceptibility is critical for safeguarding the biometric system [14]. The complicated risk of biometric spoofing is minimized by assessing the liveness of biometric features prior to authentication [15]. The main objective of this study is to identify iris liveness detection with reduced feature vectors.

The main objective of this study is to identify iris liveness detection with reduced feature vectors. Following are the novelty and the main contributions of this paper.

Initiatory utilization of ‘fragmental coefficients of Haar transformed iris image data’ as signatures in iris liveness detection;

The paper’s organization is presented herewith. Section 2 elaborates on an outline of existing methodologies. Section 3 portrays the proposed approach to iris liveness detection. The experimentation setup is put forth in Section 4. While Section 5 elaborates on the noted outcomes and the conclusions taken from the findings. Section 6 is where the discussion takes place. Finally, final thoughts, limitations, and future research suggestions are presented in Section 7.

Various strategies have been adopted to determine whether the acquired biometric traits are alive prior to the authentication. Several of the most well-known techniques are addressed in this section.

For liveness identification, Agarwal et al. [16] explored fingerprints with iris. To create a fingerprint vector function, the essential Haralick statistical characteristics use GLCM and NGTDM. The iris texture feature is utilized to improve the device’s performance. To evaluate if this model is more efficient than the current one, Agarwal used a standard dataset. GLCM has an extensive feature vector size in the current system. Iris spoofing attacks are detected using rotation-invariant features of Polar harmonic transformations and Zernike moments [5]. Spoofing assaults on numerous sensors significantly impact the system’s overall competence. The system detects attacks like iris print and contact lenses.

Thavalengal et al. created a that uses smartphones to take RGB along with NIR images of the iris and eye [17]. Identification is made using pupil localization techniques and distance measures. 4096-dimensional features are examined for feature vector generation, which is a considerable number. Although the author claims a high rate of liveness recognition, he does not work with standard datasets. Authors Fathy et al. have not examined the segmentation or normalization processes commonly utilized in Iris liveness discovery systems [13]. The original image is broken down into wavelets using Wavelet Packets (WPs). Although the author claims it is 100 percent accurate, it is not working with all genres of assaults and only covers a few spoof attacks.

Iris liveness detection will be made utilizing regional features by author Hu et al. [18]. The interaction of the properties of nearby regions is used to create regional traits. The author utilized one hundred forty-four relational measurements based on regional attributes during the experiment.

Using pupil dynamics, the author Czajka [19] created the liveness identification system. The pupil reaction is tested in this system using rapid changes in the intensity of light. In the case of the eye reacting to the variations in light intensity, it is alive; otherwise, presentation attack image. In [19], non-linear and linear SVM is employed to categorize natural reactions and impulsive oscillations. The system’s shortcoming is that it measures a variety of functions that take time. There are inaccuracies in the observation because the data utilized in this research does not include any details from elderly adults.

Author Fang et al. [20] apply many data augmentation methods to generate variability. The strategy-level and the score-level combination of fusion methods are used for Iris PAD. Bassi et al. [21] detected PAD using NIR, visible domain, cross-datasets and cross-spectrum datasets. Authors concluded that Cross-PA and cross-datasets are still challenging, as EER values above 20% in most of cases.

A technique to detect Accurate Ocular Regions was created by Naqvi et al. [22]. This solution uses deep neural network variants. The system’s evaluation considers publicly available databases. Kimura et al. [23] developed a CNN-based liveness detection System that improves model accuracy by modifying hyperparameters. “Attack Presentation Classification Error Rate [APCER]” and “Bonafede Presentation Classification Error Rate [BPCER]” metrics are taken to assess the system’s performance. The hyperparameters are all studied in this work. This method is solely effective against prints and contact lens attacks. Author [24] studied multiple transfer learning models to detect the iris liveness and concluded that EfficeintnetB7 gives highest classification accuracy.

Only a few studies were found to be robust against all sorts of spoofing assaults [3,25]. Most of the studies used a higher size feature vector. Based on these findings, it can be believed that there is a necessity for a classifier or ensembles for the detection of every sort of spoofing assault.

The iris recognition system is prone to a range of security threats. Because of these flaws, the system is less trustworthy for robust authentication applications. The study employs fragmental energy of Haar modified iris images to attempt iris liveness detection. These fragmental energies were employed as features to detect whether the iris was real or fake. Because of these characteristics, the suggested methodology does not require any pre-processing, such as segmentation, normalization, or localization, which are commonly employed by methods presented in the literature. These fragmental energies were employed as features to detect whether the iris was real or fake. Because of these characteristics, the suggested methodology does not require any pre-processing, such as segmentation, normalization, or localization, which are commonly employed by methods presented in the literature. These fragmental energies make the suggested technique faster and more accessible [26]. Resizing the iris image to 256 ^* 256 is the sole pre-processing performed in the proposed framework. The iris liveness detection process depicted in Fig. 1 is a block diagram. There are three phases in the proposed system. Resizing [pre-processing] of iris images, feature formation, and classification with iris liveness identification.

The investigative results of the proposed method are discussed in this section. The experiments were performed using an Intel (R) Core (TM) i3-6006U CPU @ 2.0 GHz, 12 GB RAM, and 6a 464-bit operating system with MATLAB R2015a as a programming platform. Clarkson LiveDet2013 (Clarkson 2013), LiveDet2015 (Clarkson 2015), and IITD Combined Spoofing datasets (IIITD CSD) were used to explore the suggested approach to iris liveness detection.

The benchmark datasets for all feature size variants are taken to test the proposed iris liveness detection method. Performance measurements such as accuracy, F-measure, precision, and recall are considered for testing versions of the proposed iris liveness detection technique.

The proposed experiment was performed using the Haar transform. The fundamental goal of experimenting with the transform domain is to learn more about how the image is split into low and high-energy parts, reducing the feature vector size and speeding up retrieval. The Haar statistic is used to transform data [27].

By applying Haar transform on Iris images, high energy coefficients of transformed iris images sized 256 ^* 256, 128 ^* 128, 64 ^* 64, 32 ^* 32, 16 ^* 16, 8 ^* 8, and 4 ^* 4 do generate feature vectors for the projected iris-liveness detection. The procedures outlined in Section 3.2 are used to create the feature vector. Seven distinct ML and ensembles of classifiers are trained using these features. These classifiers are trained using the tenfold cross-validation method to detect presentation attacks. Three benchmark datasets are taken for testing: Clarkson 2013, Clarkson 2015, and the IIITD combined spoofing database. These three datasets explain in Section 4.1. Accuracy, Precision, Recall, and F-ratio and ISO standard metrics APCER, BPCER, and ACER are utilized to compare the performance of all the variants of the suggested approach. Section 4.2 describes several performance measures.

As stated in Sections 5.1 and 5.2, feature extraction using Haar has shown outstanding average classification accuracy. For the Clarkson 2013 dataset, the highest noted iris liveness detection accuracy comes around 98.10%, with 8 ^* 8 fragmental coefficients obtained using the classifier RF. The highest average accuracy, 95.94% was achieved by 16 ^* 16 fragmental coefficients, whereas the maximum average accuracy was 96.31%, and the average ACER was around 2.63%, achieved by SVM+MLP+RF ensembles of classifiers. For Clarkson 2015, the highest accuracy achieved was 99.90% by using an SVM+RF+RT ensemble classifier. For IIITD CSD, the uppermost accuracy of 97.94% was obtained by using the RF classifier. The uppermost average accuracy was 96.80% achieved by 8 ^* 8 fragmental coefficients, whereas the utmost average accuracy was 97.05%, and average ACER was around 2.29%, achieved by SVM+NB+RF ensembles of classifiers. The findings show that our suggested approach distinguishes between the bonafide and presentation attack images artifacts using the Haar transform approach. Table 10 shows a comparison of the suggested strategy to recent studies in this area.

Compared to similar current techniques based on fragmented energy, the Haar transformation better discerns between real and artificial artifacts. The results reveal that the proposed method reduces classification error and gets better accuracy when compared to earlier ways of detecting presentation attacks using an iris liveness detection. Table 10 summarizes this information. The proposed strategy outperforms some recent existing studies. As many recent studies used the different train and test datasets, so we partition Table 10 into two parts, comparing with same datasets and different datasets. Even though some studies outperform, our approach achieved this performance with reduced feature vector size (only with 64 features).

While implementing this study, we faced a few challenges-one of them was getting access to datasets with permissions for use in experimentation. The second challenge we faced was the explorations of the proposed method with the number of test runs was time-consuming task.

The limitations of the study are: (i) it was applied only two pre-processing techniques like resizing and converting an image into grayscale; (ii) this experiment is limited to image size 256 by 256 and only grayscale images were used during this study.

The paper proposed a new method for determining iris liveness. Until now, several approaches have relied on pre-processing, such as iris segmentation, localization, and normalization; however, this method of iris liveness detection is computationally intensive. The suggested method employs Haar transforms on iris images to address this issue, obtaining fragmental coefficients as feature vectors. The Haar transformed iris image fragmental coefficients are used to train various ML and ensemble algorithms. Seven criteria are considered to compare the performance of variants of the suggested approach. Various metrics such as accuracy, precision, recall, f-measure, APCER, BPCER, and ACER are used to check the performance of the models. Presentation attack images are detected with 98.10% accuracy in the Clarkson 2013 dataset. The best accuracy for IIITD-CSD was 97.94%. The experimental results prove the effectiveness of the projected method for detecting iris spoofing attacks. The study’s main contribution is achieving a good accuracy of 98.10% with lesser feature vector size by using the fragmental coefficients of the Haar transformed iris image of size 8 ^* 8 utilizing a RF algorithm with reduced featured vector size. The reduction in considered feature vector size of iris images with improved accuracy of liveness detection is achieved by exploiting the energy compaction property of Haar transform in the proposed method. The method is tested on three available benchmark datasets for validation of results in a generic form. The cross-dataset validations are performed to prove the worth of the proposed method. The main limitations of this study are as follows: only two pre-processing techniques were applied, such as resizing and converting an image into grayscale. Moreover, this experiment is limited to image size 256 by 256 and only grayscale images are used. In future work, this framework may be extended with the best performance features. Currently, the presented work is limited to the exploration of explored Haar transform features only. However, the hybridization of transform using Haar, DCT and Kekare transforms would be an exciting exploration in the future. Moreover, the proposed framework may be applied for the liveness detection of other biometric traits, like face, fingerprints, etc. The best performance features a level fusion of fragmental coefficients of Haar may be added to this framework in future work.