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  • Open Access

    ARTICLE

    Iris Recognition Based on Multilevel Thresholding Technique and Modified Fuzzy c-Means Algorithm

    Slim Ben Chaabane1,2,*, Rafika Harrabi1,2, Anas Bushnag1, Hassene Seddik2

    Journal on Artificial Intelligence, Vol.4, No.4, pp. 201-214, 2022, DOI:10.32604/jai.2022.032850

    Abstract Biometrics represents the technology for measuring the characteristics of the human body. Biometric authentication currently allows for secure, easy, and fast access by recognizing a person based on facial, voice, and fingerprint traits. Iris authentication is one of the essential biometric methods for identifying a person. This authentication type has become popular in research and practical applications. Unlike the face and hands, the iris is an internal organ, protected and therefore less likely to be damaged. However, the number of helpful information collected from the iris is much greater than the other biometric human organs. This work proposes a new… More >

  • Open Access

    ARTICLE

    Biometric Verification System Using Hyperparameter Tuned Deep Learning Model

    Mohammad Yamin1, Saleh Bajaba2, Sarah B. Basahel3, E. Laxmi Lydia4,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 321-336, 2023, DOI:10.32604/csse.2023.034849

    Abstract Deep learning (DL) models have been useful in many computer vision, speech recognition, and natural language processing tasks in recent years. These models seem a natural fit to handle the rising number of biometric recognition problems, from cellphone authentication to airport security systems. DL approaches have recently been utilized to improve the efficiency of various biometric recognition systems. Iris recognition was considered the more reliable and accurate biometric detection method accessible. Iris recognition has been an active research region in the last few decades due to its extensive applications, from security in airports to homeland security border control. This article… More >

  • Open Access

    ARTICLE

    Novel Multimodal Biometric Feature Extraction for Precise Human Identification

    J. Vasavi1, M. S. Abirami2,*

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1349-1363, 2023, DOI:10.32604/iasc.2023.032604

    Abstract In recent years, biometric sensors are applicable for identifying important individual information and accessing the control using various identifiers by including the characteristics like a fingerprint, palm print, iris recognition, and so on. However, the precise identification of human features is still physically challenging in humans during their lifetime resulting in a variance in their appearance or features. In response to these challenges, a novel Multimodal Biometric Feature Extraction (MBFE) model is proposed to extract the features from the noisy sensor data using a modified Ranking-based Deep Convolution Neural Network (RDCNN). The proposed MBFE model enables the feature extraction from… More >

  • Open Access

    ARTICLE

    Chaotic Krill Herd with Deep Transfer Learning-Based Biometric Iris Recognition System

    Harbi Al-Mahafzah1, Tamer AbuKhalil1, Bassam A. Y. Alqaralleh2,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5703-5715, 2022, DOI:10.32604/cmc.2022.030399

    Abstract Biometric verification has become essential to authenticate the individuals in public and private places. Among several biometrics, iris has peculiar features and its working mechanism is complex in nature. The recent developments in Machine Learning and Deep Learning approaches enable the development of effective iris recognition models. With this motivation, the current study introduces a novel Chaotic Krill Herd with Deep Transfer Learning Based Biometric Iris Recognition System (CKHDTL-BIRS). The presented CKHDTL-BIRS model intends to recognize and classify iris images as a part of biometric verification. To achieve this, CKHDTL-BIRS model initially performs Median Filtering (MF)-based preprocessing and segmentation for… More >

  • Open Access

    ARTICLE

    An Optimised Defensive Technique to Recognize Adversarial Iris Images Using Curvelet Transform

    K. Meenakshi1,*, 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 >

  • Open Access

    ABSTRACT

    Evaluation of Statistical Feature Encoding Techniques on Iris Images

    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 >

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