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  • Open Access

    ARTICLE

    Biometric Finger Vein Recognition Using Evolutionary Algorithm with Deep Learning

    Mohammad Yamin1,*, Tom Gedeon2, Saleh Bajaba3, Mona M. Abusurrah4

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5659-5674, 2023, DOI:10.32604/cmc.2023.034005 - 29 April 2023

    Abstract In recent years, the demand for biometric-based human recognition methods has drastically increased to meet the privacy and security requirements. Palm prints, palm veins, finger veins, fingerprints, hand veins and other anatomic and behavioral features are utilized in the development of different biometric recognition techniques. Amongst the available biometric recognition techniques, Finger Vein Recognition (FVR) is a general technique that analyzes the patterns of finger veins to authenticate the individuals. Deep Learning (DL)-based techniques have gained immense attention in the recent years, since it accomplishes excellent outcomes in various challenging domains such as computer vision,… More >

  • Open Access

    ARTICLE

    GaitDONet: Gait Recognition Using Deep Features Optimization and Neural Network

    Muhammad Attique Khan1, Awais Khan1, Majed Alhaisoni2, Abdullah Alqahtani3, Ammar Armghan4, Sara A. Althubiti5, Fayadh Alenezi4, Senghour Mey6, Yunyoung Nam6,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5087-5103, 2023, DOI:10.32604/cmc.2023.033856 - 29 April 2023

    Abstract Human gait recognition (HGR) is the process of identifying a subject (human) based on their walking pattern. Each subject is a unique walking pattern and cannot be simulated by other subjects. But, gait recognition is not easy and makes the system difficult if any object is carried by a subject, such as a bag or coat. This article proposes an automated architecture based on deep features optimization for HGR. To our knowledge, it is the first architecture in which features are fused using multiset canonical correlation analysis (MCCA). In the proposed method, original video frames… More >

  • Open Access

    ARTICLE

    A Privacy-Preserving System Design for Digital Presence Protection

    Eric Yocam1, Ahmad Alomari2, Amjad Gawanmeh3,*, Wathiq Mansoor3

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3091-3110, 2023, DOI:10.32604/cmc.2023.032826 - 31 March 2023

    Abstract A person’s privacy has become a growing concern, given the nature of an expansive reliance on real-time video activities with video capture, stream, and storage. This paper presents an innovative system design based on a privacy-preserving model. The proposed system design is implemented by employing an enhanced capability that overcomes today’s single parameter-based access control protection mechanism for digital privacy preservation. The enhanced capability combines multiple access control parameters: facial expression, resource, environment, location, and time. The proposed system design demonstrated that a person’s facial expressions combined with a set of access control rules can More >

  • Open Access

    ARTICLE

    Multi-Path Attention Inverse Discrimination Network for Offline Signature Verification

    Xiaorui Zhang1,2,3,4,*, Yingying Wang1, Wei Sun4,5, Qi Cui6, Xindong Wei7

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3057-3071, 2023, DOI:10.32604/iasc.2023.033578 - 15 March 2023

    Abstract Signature verification, which is a method to distinguish the authenticity of signature images, is a biometric verification technique that can effectively reduce the risk of forged signatures in financial, legal, and other business environments. However, compared with ordinary images, signature images have the following characteristics: First, the strokes are slim, i.e., there is less effective information. Second, the signature changes slightly with the time, place, and mood of the signer, i.e., it has high intraclass differences. These challenges lead to the low accuracy of the existing methods based on convolutional neural networks (CNN). This study… More >

  • Open Access

    ARTICLE

    A Three-Dimensional Real-Time Gait-Based Age Detection System Using Machine Learning

    Muhammad Azhar1,*, Sehat Ullah1, Khalil Ullah2, Habib Shah3, Abdallah Namoun4, Khaliq Ur Rahman5

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 165-182, 2023, DOI:10.32604/cmc.2023.034605 - 06 February 2023

    Abstract Human biometric analysis has gotten much attention due to its widespread use in different research areas, such as security, surveillance, health, human identification, and classification. Human gait is one of the key human traits that can identify and classify humans based on their age, gender, and ethnicity. Different approaches have been proposed for the estimation of human age based on gait so far. However, challenges are there, for which an efficient, low-cost technique or algorithm is needed. In this paper, we propose a three-dimensional real-time gait-based age detection system using a machine learning approach. The… More >

  • Open Access

    ARTICLE

    One-Class Arabic Signature Verification: A Progressive Fusion of Optimal Features

    Ansam A. Abdulhussien1,2,*, Mohammad F. Nasrudin1, Saad M. Darwish3, Zaid A. Alyasseri1

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 219-242, 2023, DOI:10.32604/cmc.2023.033331 - 06 February 2023

    Abstract Signature verification is regarded as the most beneficial behavioral characteristic-based biometric feature in security and fraud protection. It is also a popular biometric authentication technology in forensic and commercial transactions due to its various advantages, including noninvasiveness, user-friendliness, and social and legal acceptability. According to the literature, extensive research has been conducted on signature verification systems in a variety of languages, including English, Hindi, Bangla, and Chinese. However, the Arabic Offline Signature Verification (OSV) system is still a challenging issue that has not been investigated as much by researchers due to the Arabic script being… 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 - 20 January 2023

    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… More >

  • Open Access

    ARTICLE

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

    Smita Khade1, Shilpa Gite1,2,*, Sudeep D. Thepade3, Biswajeet Pradhan4,5,*, Abdullah Alamri6

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 323-345, 2023, DOI:10.32604/cmes.2023.023674 - 05 January 2023

    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… More > Graphic Abstract

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

  • 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 - 05 January 2023

    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… More >

  • Open Access

    ARTICLE

    Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

    S. Aanjanadevi1,*, S. Aanjankumar2, K. R. Ramela3, V. Palanisamy4

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2351-2362, 2023, DOI:10.32604/csse.2023.031893 - 21 December 2022

    Abstract Due to the enormous usage of the internet for transmission of data over a network, security and authenticity become major risks. Major challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database server. To describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical (Face, fingerprint, Ear etc.) and behavioural (Gesture, Voice, tying etc.) by means of matching and verification process. In this work, biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices… More >

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