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Artykuły w czasopismach na temat „Biometric Fusion System”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Mrs., M. Tamil Selvi*1 &. Mrs. A. Sumathi 2. "FUSION TECHNIQUES FOR BIMODAL AND MULTIMODAL BIOMETRIC SYSTEM AUTHENTICATION." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 9 (2017): 40–45. https://doi.org/10.5281/zenodo.884845.

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Biometrics is the one of the security mechanism for computer network security. It is science and technology of measuring and analyzing biological data of human body, extracting details from the acquired data, and comparing the data to the stored in database. Existing system is unimodal biometric system. The proposed system is supported fusion techniques multimodal biometric system. However Multimodal biometrics requires storage of multiple biometric templates for each user, which results in increased risk to user privacy and system security. This paper will discuss the concept off biometrics,

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Mushtaque, Ahmed. "BIOMETRICS SYSTEM ACKNOWLEDGEMNT BASED ON DATA FUSION." International Journal of Research – Granthaalayah 4, no. 3 (2017): 85–91. https://doi.org/10.5281/zenodo.847003.

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In these days lot of systems have need an efficient and reliable biometrics recognition system. By considering the importance of biometric systems in global world, in this paper we are discussing the importance of a new era of verification. Applications of Biometric systems in accordance to Data fusion and what is the future of biometric systems.

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Abdoul, Kamal Assouma, Djara Tahirou, and Sobabe Abdou-Aziz. "Multi-Biometrics: Survey and Projection of a New Biometric System." International Journal of Engineering and Advanced Technology (IJEAT) 12, no. 3 (2023): 80–87. https://doi.org/10.35940/ijeat.C4008.0212323.

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<strong>Abstract: </strong>Multi-biometric systems using feature-level fusion allow more accuracy and reliability in recognition performance than uni-biometric systems. But in practice, this type of fusion is difficult to implement especially when we are facing heterogeneous biometric modalities or incompatible features. The major challenge of feature fusion is to produce a representation of each modality with an excellent level of discrimination. Beyond pure biometric modalities, the use of metadata has proven to improve the performance of biometric systems. In view of these findings, our wor

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Assouma, Abdoul Kamal, Tahirou Djara, and Abdou-Aziz Sobabe. "Multi-Biometrics: Survey and Projection of a New Biometric System." International Journal of Engineering and Advanced Technology 12, no. 3 (2023): 80–87. http://dx.doi.org/10.35940/ijeat.c4008.0212323.

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Multi-biometric systems using feature-level fusion allow more accuracy and reliability in recognition performance than uni-biometric systems. But in practice, this type of fusion is difficult to implement especially when we are facing heterogeneous biometric modalities or incompatible features. The major challenge of feature fusion is to produce a representation of each modality with an excellent level of discrimination. Beyond pure biometric modalities, the use of metadata has proven to improve the performance of biometric systems. In view of these findings, our work focuses on multi-origin b

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Medjahed, Chahreddine, Abdellatif Rahmoun, Christophe Charrier, and Freha Mezzoudj. "A deep learning-based multimodal biometric system using score fusion." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 65. http://dx.doi.org/10.11591/ijai.v11.i1.pp65-80.

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Recent trends in artificial intelligence tools-based biometrics have overwhelming attention to security matters. The hybrid approaches are motivated by the fact that they combine mutual strengths and they overcome their limitations. Such approaches are being applied to the fields of biomedical engineering. A biometric system uses behavioural or physiological characteristics to identify an individual. The fusion of two or more of these biometric unique characteristics contributes to improving the security and overcomes the drawbacks of unimodal biometric-based security systems. This work propos

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Chahreddine, Medjahed, Rahmoun Abdellatif, Charrier Christophe, and Mezzoudj Freha. "A deep learning-based multimodal biometric system using score fusion." International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 65–80. https://doi.org/10.11591/ijai.v11.i1.pp65-80.

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Recent trends in artificial intelligence tools-based biometrics have overwhelming attention to security matters. The hybrid approaches are motivated by the fact that they combine mutual strengths and they overcome their limitations. Such approaches are being applied to the fields of biomedical engineering. A biometric system uses behavioural or physiological characteristics to identify an individual. The fusion of two or more of these biometric unique characteristics contributes to improving the security and overcomes the drawbacks of unimodal biometric-based security systems. This work propos

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Omar, Bayan, Hamsa D. Majeed, Siti Zaiton Mohd Hashim, and Muzhir Al-Ani. "New Feature-level Algorithm for a Face-fingerprint Integral Multi-biometrics Identification System." UHD Journal of Science and Technology 6, no. 1 (2022): 12–20. http://dx.doi.org/10.21928/uhdjst.v6n1y2022.pp12-20.

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This article delves into the power of multi-biometric fusion for individual identification. a new feature-level algorithm is proposed that is the Dis-Eigen algorithm. Here, a feature-fusion framework is proposed for attaining better accuracy when identifying individuals for multiple biometrics. The framework, therefore, underpins the new multi-biometric system as it guides multi-biometric fusion applications at the feature phase for identifying individuals. In this regard, the Face-fingerprints of 20 individuals represented by 160 images were used in this framework . Experimental resultants of

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Snehlata, Barde. "Multimodal biometrics system with face, ear, and foot fusion techniques." i-manager’s Journal on Pattern Recognition 10, no. 2 (2023): 27. http://dx.doi.org/10.26634/jpr.10.2.20352.

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Biometrics, as an identification method, is used for various applications, particularly in security technologies. The integration of multiple biometric sources aims to overcome limitations observed in unimodal systems, enhancing recognition accuracy. Fusion techniques, categorized into sensor level, feature level, matching score level, decision level, and rank level, are explored to optimize the combination of information from different modalities. Various fusion schemes, such as feature-level fusion, decision-level fusion, and hybrid systems, are investigated for their effectiveness in integr

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Ghadeer, Ibrahim Maki, and Basim Abed Sarah. "Multimodal Biometric System Fusion Using Fingerprint and Iris with Convolutional Neural Network." Engineering and Technology Journal 9, no. 09 (2024): 5140–47. https://doi.org/10.5281/zenodo.13768197.

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Biometric sensing technology became everyday life frequent component as a result of world requirement for info security and safety legislation. A strong and efficient individual authentication has appeared because of new developments in multimodal biometrics. Multimodal biometrics integrates different biological traits in trying for creating considerable effect on identification performance. Latent fingerprint biometrics refer to effective human identification system for criminals given the accessible crime evidence shreds. Although, biometric trait restrictions like intra-class variation, sen

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Sujana, S., and V. S. K. Reddy. "Comparison of levels and fusion approaches for multimodal biometrics." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (2021): 791. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp791-801.

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The biometric-based authentication system occupies maximal space in the field of security administration. Biometric applications are swiftly accelerating in day-to-day life such as computer login, smart homes, online banking, hospitals, border areas, industries, forensics, e-voting attendance system and investigation of crime. A reliable and accurate recognition body can be achieved with multimodal biometric methodologies. In this paper, we discuss starting with an introduction to biometric systems followed by their classification, and advantages as well as disadvantages. In today’s world, mos

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Sujana, S., and V. S. K. Reddy. "Comparison of levels and fusion approaches for multimodal biometrics." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (2021): 791–801. https://doi.org/10.11591/ijeecs.v23.i2.pp791-801.

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The biometric-based authentication system occupies maximal space in the field of security administration. Biometric applications are swiftly accelerating in day-to-day life such as computer login, smart homes, online banking, hospitals, border areas, industries, forensics, e-voting attendance system and investigation of crime. A reliable and accurate recognition body can be achieved with multimodal biometric methodologies. In this paper, we discuss starting with an introduction to biometric systems followed by their classification, and advantages as well as disadvantages. In today’s worl

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Shinde, Prashant Pandurang, and Amol Sable. "Hybridization for Classification and Identification of Individuals using Multimodal Biometric Systems." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 2126–32. https://doi.org/10.35940/ijeat.D7249.049420.

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Biometric recognition systems use certain human characteristics such as voice, facial features, fingerprint, iris or hand geometry to identify an individual or verify their identity. These systems have been developed individually for each of these biometric modalities until they achieve remarkable levels of performance. Biometrics is a measure of biological characteristics for the identification or authentication of an individual based on some of its characteristics. Although biometric recognition techniques promise to be very effective, At present, we can not guarantee an excellent identifica

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Miss., Kamble Sunayana Nivrutti, Gund. V. D. Prof., and Kazi K. S. Prof. "Multimodal Biometrics Authentication System using Fusion of Fingerprint and Iris." International Journal of Trend in Scientific Research and Development 2, no. 6 (2018): 1282–86. https://doi.org/10.31142/ijtsrd18861.

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In the present era of information technology, there is a need to implement authentication and authorization techniques for security of resources. There are number of ways to prove authentication and authorization. But the biometric authentication beat all other techniques. Biometric techniques prove the authenticity or authorization of a human being based on his her physiological or behavioural traits. It also protects resources access from unauthorized users. We will develop a multimodal biometric identification system that represents a valid alternative to conventional approaches. In biometr

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S. Raju, A., and V. Udayashankara. "A Survey on Unimodal, Multimodal Biometrics and Its Fusion Techniques." International Journal of Engineering & Technology 7, no. 4.36 (2018): 689. http://dx.doi.org/10.14419/ijet.v7i4.36.24224.

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Presently, a variety of biometric modalities are applied to perform human identification or user verification. Unimodal biometric systems (UBS) is a technique which guarantees authentication information by processing distinctive characteristic sequences and these are fetched out from individuals. However, the performance of unimodal biometric systems restricted in terms of susceptibility to spoof attacks, non-universality, large intra-user variations, and noise in sensed data. The Multimodal biometric systems defeat various limitations of unimodal biometric systems as the sources of different

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Herbadji, Abderrahmane, Zahid Akhtar, Kamran Siddique, et al. "Combining Multiple Biometric Traits Using Asymmetric Aggregation Operators for Improved Person Recognition." Symmetry 12, no. 3 (2020): 444. http://dx.doi.org/10.3390/sym12030444.

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Biometrics is a scientific technology to recognize a person using their physical, behavior or chemical attributes. Biometrics is nowadays widely being used in several daily applications ranging from smart device user authentication to border crossing. A system that uses a single source of biometric information (e.g., single fingerprint) to recognize people is known as unimodal or unibiometrics system. Whereas, the system that consolidates data from multiple biometric sources of information (e.g., face and fingerprint) is called multimodal or multibiometrics system. Multibiometrics systems can

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Alay, Nada, and Heyam H. Al-Baity. "Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits." Sensors 20, no. 19 (2020): 5523. http://dx.doi.org/10.3390/s20195523.

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With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is b

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OMOTOSHO, LAWRENCE, IBRAHIM OGUNDOYIN, OLAJIDE ADEBAYO, and JOSHUA OYENIYI. "AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK." Journal of Engineering Studies and Research 27, no. 2 (2021): 73–81. http://dx.doi.org/10.29081/jesr.v27i2.276.

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Multimodal biometric system combines more than one biometric modality into a single method in order, to overcome the limitations of unimodal biometrics system. In multimodal biometrics system, the utilization of different algorithms for feature extraction, fusion at feature level and classification often to complexity and make fused biometrics features larger in dimensions. In this paper, we developed a face-iris multimodal biometric recognition system based on convolutional neural network for feature extraction, fusion at feature level, training and matching to reduce dimensionality, error ra

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Wang, Yang, Dekai Shi, and Weibin Zhou. "Convolutional Neural Network Approach Based on Multimodal Biometric System with Fusion of Face and Finger Vein Features." Sensors 22, no. 16 (2022): 6039. http://dx.doi.org/10.3390/s22166039.

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In today’s information age, how to accurately identify a person’s identity and protect information security has become a hot topic of people from all walks of life. At present, a more convenient and secure solution to identity identification is undoubtedly biometric identification, but a single biometric identification cannot support increasingly complex and diversified authentication scenarios. Using multimodal biometric technology can improve the accuracy and safety of identification. This paper proposes a biometric method based on finger vein and face bimodal feature layer fusion, which use

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Shinde, Krishna, and Sumegh Tharewal. "Development of Face and Signature Fusion Technology for Biometrics Authentication." International Journal of Emerging Research in Management and Technology 6, no. 9 (2018): 61. http://dx.doi.org/10.23956/ijermt.v6i9.86.

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The Biometrics system is getting popularity since last decade As per Information Technology industry demand. This techn-ology are satisfy authentication and authorization process needs. But the unimodal biometric system have own limitations. the limitation of unimodal, we can choosing the approach of multimodal biometric system. In this research paper choose the physiological model for face recognition and behavioural model for signature recognition. The recognition of face and signature used match score level fusion. In this fusion technology for secured authentication of person

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Sindhu, B., Y. Karthik, B. Durga Devi, A. Raviteja, and R. Vivek. "Face-Gaze Biometric Fusion for Enhanced Personal Authentication." Indian Journal Of Science And Technology 17, no. 44 (2024): 4611–18. https://doi.org/10.17485/ijst/v17i44.3512.

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Objectives: To develop a robust authentication system by combining two authentication modals. The system aims to enhance the security by integrating the gaze-based biometrics in the authentication process, which is difficult to perform spoofing attacks. Methods: The system uses gaze-tracking and face recognition modals to authenticate the user. The user will enter his/her credentials, then the first step of authentication will start where the face recognition modal will authenticate the user by detecting the user’s face, if one is not authenticated in this phase then the user will be omitted.

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Herbadji, Abderrahmane, Noubeil Guermat, Lahcene Ziet, Zahid Akhtar, Mohamed Cheniti, and Djamel Herbadji. "Contactless Multi-biometric System Using Fingerprint and Palmprint Selfies." Traitement du Signal 37, no. 6 (2020): 889–97. http://dx.doi.org/10.18280/ts.370602.

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Due to the COVID-19 pandemic, automated contactless person identification based on the human hand has become very vital and an appealing biometric trait. Since, people are expected to cover their faces with masks, and advised avoiding touching surfaces. It is well-known that usually contact-based hand biometrics suffer from issues like deformation due to uneven distribution of pressure or improper placement on sensor, and hygienic concerns. Whereas, to mitigate such problems, contactless imaging is expected to collect the hand biometrics information without any deformation and leading to highe

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Rahman, Md Wasiur, Fatema Tuz Zohra, and Marina L. Gavrilova. "Score Level and Rank Level Fusion for Kinect-Based Multi-Modal Biometric System." Journal of Artificial Intelligence and Soft Computing Research 9, no. 3 (2019): 167–76. http://dx.doi.org/10.2478/jaiscr-2019-0001.

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Abstract Computational intelligence firmly made its way into the areas of consumer applications, banking, education, social networks, and security. Among all the applications, biometric systems play a significant role in ensuring an uncompromised and secure access to resources and facilities. This article presents a first multimodal biometric system that combines KINECT gait modality with KINECT face modality utilizing the rank level and the score level fusion. For the KINECT gait modality, a new approach is proposed based on the skeletal information processing. The gait cycle is calculated us

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D S, Dr Dinesh Kumar. "Human Authentication using Face, Voice and Fingerprint Biometrics." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 853–62. http://dx.doi.org/10.22214/ijraset.2021.36381.

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Multimodal biometric approaches are growing in importance for personal verification and identification, since they provide better recognition results and hence improve security compared to biometrics based on a single modality. In this project, we present a multimodal biometric system that is based on the fusion of face, voice and fingerprint biometrics. For face recognition, we employ Haar Cascade Algorithm, while minutiae extraction is used for fingerprint recognition and we will be having a stored code word for the voice authentication, if any of these two authentication becomes true, the s

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Channegowda, Arjun Benagatte, and H. N. Prakash. "Multimodal biometrics of fingerprint and signature recognition using multi-level feature fusion and deep learning techniques." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (2021): 187. http://dx.doi.org/10.11591/ijeecs.v22.i1.pp187-195.

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Providing security in biometrics is the major challenging task in the current situation. A lot of research work is going on in this area. Security can be more tightened by using complex security systems, like by using more than one biometric trait for recognition. In this paper multimodal biometric models are developed to improve the recognition rate of a person. The combination of physiological and behavioral biometrics characteristics is used in this work. Fingerprint and signature biometrics characteristics are used to develop a multimodal recognition system. Histograms of oriented gradient

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Channegowda, Arjun Benagatte, and H. N. Prakash. "Multimodal biometrics of fingerprint and signature recognition using multi-level feature fusion and deep learning techniques." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (2021): 187–95. https://doi.org/10.11591/ijeecs.v22.i1.pp187-195.

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Providing security in biometrics is the major challenging task in the current situation. A lot of research work is going on in this area. Security can be more tightened by using complex security systems, like by using more than one biometric trait for recognition. In this paper multimodal biometric models are developed to improve the recognition rate of a person. The combination of physiological and behavioral biometrics characteristics is used in this work. Fingerprint and Signature biometrics characteristics are used to develop a multimodal recognition system. Histograms of oriented gradient

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RÉDA, Adjoudj, Belhıa SOUAAD, Allal ANIS, and Bahram TAYEB. "Intelligent Integration and Fusion of Multimodal Biometric Systems." Eurasia Proceedings of Science Technology Engineering and Mathematics 26 (December 30, 2023): 287–94. http://dx.doi.org/10.55549/epstem.1409583.

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The propagation and the frightening expansion of frauds due essentially to the easy access to high technologies make it difficult or sometimes impossible to detect these frauds and impostures. Therefore, it has become important, if not urgent, to develop identification techniques and tools that are more robust to attacks, more precise and more efficient. There are currently in Europe and some American countries very efficient biometric systems that combine two (2) modalities (photos and fingerprints in the case of biometric passports), but they remain very vulnerable, notably because of acquis

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Singh, Law Kumar, Munish Khanna, and Hitendra Garg. "Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features." International Journal of Information System Modeling and Design 11, no. 1 (2020): 37–57. http://dx.doi.org/10.4018/ijismd.2020010103.

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Multimodal biometrics refers to the exploiting combination of two or more biometric modalities in an identification of a system. Fingerprint, face, retina, iris, hand geometry, DNA, and palm print are physiological traits while voice, signature, keystrokes, gait are behavioural traits used for identification by a system. Single biometric features like faces, fingerprints, irises, retinas, etc., deteriorate or change with time, environment, user mode, physiological defects, and circumstance therefore integrating multi features of biometric traits increase robustness of the system. The proposed

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Channegowda, Arjun Benagatte, and Hebbakavadi Nanjundaiah Prakash. "Image fusion by discrete wavelet transform for multimodal biometric recognition." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 229. http://dx.doi.org/10.11591/ijai.v11.i1.pp229-237.

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In today’s world, security plays a crucial role in almost all applications. Providing security to a huge population is a more challenging task. Biometric security is the key player in such type of situation. Using a biometric-based security system more secure application can be built because it is tough to steal or forge. The unimodal biometric system uses only one biometric modality where some of the limitations will arise. For example, if we use fingerprints due to oiliness or scratches, the finger recognition rate may reduce. In order to overcome the drawbacks of unimodal biometrics, multim

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Arjun, Benagatte Channegowda, and Nanjundaiah Prakash Hebbakavadi. "Image fusion by discrete wavelet transform for multimodal biometric recognition." International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 229–37. https://doi.org/10.11591/ijai.v11.i1.pp229-237.

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In today’s world, security plays a crucial role in almost all applications. Providing security to a huge population is a more challenging task. Biometric security is the key player in such type of situation. Using a biometric-based security system more secure application can be built because it is tough to steal or forge. The unimodal biometric system uses only one biometric modality where some of the limitations will arise. For example, if we use fingerprints due to oiliness or scratches, the finger recognition rate may reduce. In order to overcome the drawbacks of unimodal biometrics,

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Joseph, Annie Anak, Alex Ng Ho Lian, Kuryati Kipli, et al. "Person Verification Based on Multimodal Biometric Recognition." Pertanika Journal of Science and Technology 30, no. 1 (2021): 161–83. http://dx.doi.org/10.47836/pjst.30.1.09.

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Nowadays, person recognition has received significant attention due to broad applications in the security system. However, most person recognition systems are implemented based on unimodal biometrics such as face recognition or voice recognition. Biometric systems that adopted unimodal have limitations, mainly when the data contains outliers and corrupted datasets. Multimodal biometric systems grab researchers’ consideration due to their superiority, such as better security than the unimodal biometric system and outstanding recognition efficiency. Therefore, the multimodal biometric system bas

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Sedik, Ahmed, Ahmed A. Abd El-Latif, Mudasir Ahmad Wani, Fathi E. Abd El-Samie, Nariman Abdel-Salam Bauomy, and Fatma G. Hashad. "Efficient Multi-Biometric Secure-Storage Scheme Based on Deep Learning and Crypto-Mapping Techniques." Mathematics 11, no. 3 (2023): 703. http://dx.doi.org/10.3390/math11030703.

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Cybersecurity has been one of the interesting research fields that attract researchers to investigate new approaches. One of the recent research trends in this field is cancelable biometric template generation, which depends on the storage of a cipher (cancelable) template instead of the original biometric template. This trend ensures the confidential and secure storage of the biometrics of a certain individual. This paper presents a cancelable multi-biometric system based on deep fusion and wavelet transformations. The deep fusion part is based on convolution (Conv.), convolution transpose (C

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Ammour, Basma, Larbi Boubchir, Toufik Bouden, and Messaoud Ramdani. "Face–Iris Multimodal Biometric Identification System." Electronics 9, no. 1 (2020): 85. http://dx.doi.org/10.3390/electronics9010085.

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Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an

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Paul, Padma P., and Marina L. Gavrilova. "Cancelable Fusion of Face and Ear for Secure Multi-Biometric Template." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 3 (2013): 80–94. http://dx.doi.org/10.4018/ijcini.2013070105.

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Biometric fusion to achieve multimodality has emerged as a highly successful new approach to combat problems of unimodal biometric system such as intraclass variability, interclass similarity, data quality, non-universality, and sensitivity to noise. The authors have proposed new type of biometric fusion called cancelable fusion. The idea behind the cancelable biometric or cancelability is to transform a biometric data or feature into a new one so that the stored biometric template can be easily changed in a biometric security system. Cancelable fusion does the fusion of multiple biometric tra

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Singh, Law Kumar, Munish Khanna, Shankar Thawkar, and Jagadeesh Gopal. "Robustness for Authentication of the Human Using Face, Ear, and Gait Multimodal Biometric System." International Journal of Information System Modeling and Design 12, no. 1 (2021): 39–72. http://dx.doi.org/10.4018/ijismd.2021010103.

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Biometrics is the science that deals with personal human physiological and behavioral characteristics such as fingerprints, handprints, iris, voice, face recognition, signature recognition, ear recognition, and gait recognition. Recognition using a single trait has several problems and multimodal biometrics system is one of the solutions. In this work, the novel and imperative biometric feature gait is fused with face and ear biometric features for authentication and to overcome problems of the unimodal biometric recognition system. The authors have also applied various normalization methods t

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A. El_Rahman, Sahar, and Ala Saleh Alluhaidan. "Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments." PLOS ONE 19, no. 2 (2024): e0291084. http://dx.doi.org/10.1371/journal.pone.0291084.

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In the field of data security, biometric security is a significant emerging concern. The multimodal biometrics system with enhanced accuracy and detection rate for smart environments is still a significant challenge. The fusion of an electrocardiogram (ECG) signal with a fingerprint is an effective multimodal recognition system. In this work, unimodal and multimodal biometric systems using Convolutional Neural Network (CNN) are conducted and compared with traditional methods using different levels of fusion of fingerprint and ECG signal. This study is concerned with the evaluation of the effec

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Karthi, G., and M. Ezhilarasan. "Multi biometric Template Protection using Hybrid Technique." International Journal of Engineering & Technology 7, no. 4 (2018): 2609. http://dx.doi.org/10.14419/ijet.v7i4.11485.

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Recently, multi-biometrics system has been the important identification system for providing authentication mechanism. In this pa-per, the multi-biometric recognition system uses multiple traits (face, iris and fingerprint) for authentication. The features are extracted from the traits and feature level fusion technique is applied to the individual features traits to form a fused feature. Protection of these biometrics features against various attacks points is an important concern for authentication process. One such attack is the modification of stored template, which largely affects the per

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Boucetta, Aldjia, and Kamal Eddine Melkemi. "Score Level Fusion of Palmprint, Face and Iris Using Adaptive PSO." International Journal of Applied Metaheuristic Computing 10, no. 3 (2019): 175–94. http://dx.doi.org/10.4018/ijamc.2019070109.

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Systems that use unimodal biometrics often suffer from various drawbacks such as noise in sensed data, variations that are due to intra class, nonuniversality, spoof attacks, restricted degrees of freedom and high error rates. These limitations can be solved effectively by combining two or more biometric modalities. In this article, a multimodal biometric fusion system is presented that combines palmprint, face and iris traits. The biometric fusion is performed at the score level in order to improve the accuracy of the system. Scores obtained from the three classifiers are fused using adaptive

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Arjun, B. C., and N. Prakash H. "Multimodal Biometric Recognition: Fusion of Modified Adaptive Bilinear Interpolation Data Samples of Face and Signature using Local Binary Pattern Features." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 3111–20. https://doi.org/10.35940/ijeat.C6117.029320.

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Biometric based authentication systems use particular person characteristics which might be based on either behavior like voice, signature etc. or body structure like face, iris, palm print, fingerprint, etc. The performance of any unimodal biometric arrangement is depending on elements like surroundings, atmosphere, and sensor precision. Also, there are numerous trait unique demanding situations which include pose, expression, growing old and so forth for face reputation, occlusion and acquisition related problems for iris and terrible high-quality and social popularity related troubles for f

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Singh, Sandeep Pratap, and Shamik Tiwari. "A Dual Multimodal Biometric Authentication System Based on WOA-ANN and SSA-DBN Techniques." Sci 5, no. 1 (2023): 10. http://dx.doi.org/10.3390/sci5010010.

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Identity management describes a problem by providing the authorized owners with safe and simple access to information and solutions for specific identification processes. The shortcomings of the unimodal systems have been addressed by the introduction of multimodal biometric systems. The use of multimodal systems has increased the biometric system’s overall recognition rate. A new degree of fusion, known as an intelligent Dual Multimodal Biometric Authentication Scheme, is established in this study. In the proposed work, two multimodal biometric systems are developed by combining three unimoda

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Zhifang Wang, Shuangshuang Wang, and Qun Ding. "Security of Multimodal Biometric Fusion System." International Journal of Digital Content Technology and its Applications 5, no. 4 (2011): 264–70. http://dx.doi.org/10.4156/jdcta.vol5.issue4.32.

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G S, Monisha, M. Hari Krishnan, Vetri Selvan M, G. Nirmala, and Yogashree G S. "Visual Tracking Based on Human Feature Extraction from Surveillance Video for Human Recognition." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7 (2023): 133–41. http://dx.doi.org/10.17762/ijritcc.v11i7.7838.

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A multimodal human identification system based on face and body recognition may be made available for effective biometric authentication. The outcomes are achieved by extracting facial recognition characteristics using several extraction techniques, including Eigen-face and Principle Component Analysis (PCA). Systems for authenticating people using their bodies and faces are implemented using artificial neural networks (ANN) and genetic optimization techniques as classifiers. Through feature fusion and scores fusion, the biometric systems for the human body and face are merged to create a sing

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Dipti Yadav. "Improving Recognition Accuracy in Multimodal Biometric Systems: A Study on Facial Traits and Fusion Strategies." Journal of Information Systems Engineering and Management 10, no. 12s (2025): 166–78. https://doi.org/10.52783/jisem.v10i12s.1771.

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Multimodal biometric systems are gaining prominence for their ability to enhance recognition accuracy and system security by integrating multiple biometric modalities. This study focuses on improving recognition accuracy through the effective utilization of facial traits and fusion strategies in multimodal systems. Facial traits, including features such as eyes, nose, lips, and chin, offer unique identification markers, but their performance can be hindered by factors like aging, lighting conditions, and variations in pose. To address these challenges, the integration of facial traits with oth

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Damousis, I. G., and S. Argyropoulos. "Four Machine Learning Algorithms for Biometrics Fusion: A Comparative Study." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/242401.

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We examine the efficiency of four machine learning algorithms for the fusion of several biometrics modalities to create a multimodal biometrics security system. The algorithms examined are Gaussian Mixture Models (GMMs), Artificial Neural Networks (ANNs), Fuzzy Expert Systems (FESs), and Support Vector Machines (SVMs). The fusion of biometrics leads to security systems that exhibit higher recognition rates and lower false alarms compared to unimodal biometric security systems. Supervised learning was carried out using a number of patterns from a well-known benchmark biometrics database, and th

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Akintunde, O. A., A. B. Adetunji, O. D. Fenwa, J. P. Oguntoye, D. S. Olayiwola, and A. J. Adeleke. "Comparative analysis of score level fusion techniques in multi-biometric system." LAUTECH Journal of Engineering and Technology 19, no. 1 (2025): 128–41. https://doi.org/10.36108/laujet/5202.91.0121.

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Multimodal biometric systems have garnered significant interest from researchers owing to their applicability in security and access control. Despite the development of numerous score level fusion techniques for multimodal biometrics, most of them have concentrated solely on enhancing fusion accuracy, neglecting the potential advantages of various score level techniques. This research investigates the comparative performance of four different score level fusion approaches for multimodal recognition of combined face and fingerprints biometrics: Product rule, Weighted Sum rule, Simple Sum rule,

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B, Sindhu, Karthik Y, Durga Devi B, Raviteja A, and Vivek R. "Face-Gaze Biometric Fusion for Enhanced Personal Authentication." Indian Journal of Science and Technology 17, no. 44 (2024): 4611–18. https://doi.org/10.17485/IJST/v17i44.3512.

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Abstract <strong>Objectives:</strong> To develop a robust authentication system by combining two authentication modals. The system aims to enhance the security by integrating the gaze-based biometrics in the authentication process, which is difficult to perform spoofing attacks. <strong>Methods:</strong> The system uses gaze-tracking and face recognition modals to authenticate the user. The user will enter his/her credentials, then the first step of authentication will start where the face recognition modal will authenticate the user by detecting the user’s face, if one is

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Rajagopal, Gayathri, and Ramamoorthy Palaniswamy. "Performance Evaluation of Multimodal Multifeature Authentication System UsingKNN Classification." Scientific World Journal 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/762341.

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This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level

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Pathak, Mrunal. "Multimodal Biometric Authentication for Smartphones." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 1559–68. http://dx.doi.org/10.22214/ijraset.2021.39569.

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Abstract: Smartphones have become a crucial way of storing sensitive information; therefore, the user's privacy needs to be highly secured. This can be accomplished by employing the most reliable and accurate biometric identification system available currently which is, Eye recognition. However, the unimodal eye biometric system is not able to qualify the level of acceptability, speed, and reliability needed. There are other limitations such as constrained authentication in real time applications due to noise in sensed data, spoof attacks, data quality, lack of distinctiveness, restricted amou

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Atenco, Juan Carlos, Juan Carlos Moreno, Juan Manuel Ramirez, Rene Arechiga, Pilar Gomez, and Rigoberto Fonseca. "Bimodal biometric recognition system using Convolutional Neural Networks and fusion of deep audiovisual feature vectors." International Journal of Combinatorial Optimization Problems and Informatics 15, no. 3 (2024): 4–14. http://dx.doi.org/10.61467/2007.1558.2024.v15i2.289.

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In recent years, interest has grown in the use biometric systems for identity authentication tasks in digital services, forensic and security applications. A unimodal system (employing a single biometric trait) with high performance is still vulnerable to falsification attacks such as spoofing. For this reason, research on multimodal biometrics (employing various biometric traits) has increased to reinforce security, increase recognition performance, and make false identity authentication more difficult. In this paper, we propose a bimodal system that combines speech and face modalities by con

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Szczuko, Piotr, Arkadiusz Harasimiuk, and Andrzej Czyżewski. "Evaluation of Decision Fusion Methods for Multimodal Biometrics in the Banking Application." Sensors 22, no. 6 (2022): 2356. http://dx.doi.org/10.3390/s22062356.

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An evaluation of decision fusion methods based on Dempster-Shafer Theory (DST) and its modifications is presented in the article, studied over real biometric data from the engineered multimodal banking client verification system. First, the approaches for multimodal biometric data fusion for verification are explained. Then the proposed implementation of comparison scores fusion is presented, including details on the application of DST, required modifications, base probability, and mass conversions. Next, the biometric verification process is described, and the engineered biometric banking sys

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Juluri, Samatha, and Madhavi G. "SecureSense: Enhancing Person Verification through Multimodal Biometrics for Robust Authentication." Scalable Computing: Practice and Experience 25, no. 2 (2024): 1040–54. http://dx.doi.org/10.12694/scpe.v25i2.2524.

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Biometrics provide enhanced security and convenience compared to conventional methods of individual authentication. A more robust and effective method of individual authentication has emerged due to recent advancements in multimodal biometrics. Unimodal systems offer lower security and lack the robustness found in multimodal biometric systems. The research paper introduces a novel approach, employing multiple biometric modalities, including face, fingerprint, and iris, to authenticate users in a multimodal biometric system. The paper proposes the ”Secure Sense” framework, which combines multip

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