Smita Khade¹, Shilpa Gite^1,2,*, Sudeep D. Thepade³, Biswajeet Pradhan^4,5,*, Abdullah Alamri⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 323-345, 2023, DOI:10.32604/cmes.2023.023674

Abstract 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.… More > Graphic Abstract

K. Meenakshi^1,*, G. Maragatham²

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 627-643, 2023, DOI:10.32604/iasc.2023.026961

Abstract Deep Learning is one of the most popular computer science techniques, with applications in natural language processing, image processing, pattern identification, and various other fields. Despite the success of these deep learning algorithms in multiple scenarios, such as spam detection, malware detection, object detection and tracking, face recognition, and automatic driving, these algorithms and their associated training data are rather vulnerable to numerous security threats. These threats ultimately result in significant performance degradation. Moreover, the supervised based learning models are affected by manipulated data known as adversarial examples, which are images with a particular level of noise that is invisible… More >

Fatma Mallouli

Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 243-248, 2019, DOI:10.31209/2019.100000069

Abstract Density estimation via Gaussian mixture modelling has been successfully applied to image segmentation. In this paper, we have learned distributions mixture model to the pixel of an iris image as training data. We introduce the proposed algorithm by adapting the Expectation-Maximization (EM) algorithm. To further improve the accuracy for iris segmentation, we consider the EM algorithm in Markovian and non Markovian cases. Simulated data proves the accuracy of our algorithm. The proposed method is tested on a subset of the CASIA database by Chinese Academy of Sciences Institute of Automation-IrisTwins. The obtained results have shown a significant improvement of our… More >

Chowhan S.S.¹, G.N. Shinde²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.9, No.1, pp. 67-74, 2009, DOI:10.3970/icces.2009.009.067

Abstract Feature selection, often used as a pre-processing step to machine learning, is designed to reduce dimensionality, eliminate irrelevant data and improve accuracy. Iris Basis is our first attempt to reduce the dimensionality of the problem while focusing only on parts of the scene that effectively identify the individual. Independent Component Analysis (ICA) is to extract iris feature to recognize iris pattern. Principal Component Analysis (PCA) is a dimension-reduction tool that can be used to reduce a large set of variables to a small set that still contains most of the information in the large set. Image quality is very important… More >