Continuous user authentication based score level fusion with hybrid optimization

This paper investigates the performance of a bimodal biometric system using fusion of shape and texture. We propose several new hand shape features that can be used to represent the hand shape and improve the performance for hand shape based user authentication. We also demonstrate the usefulness of Discrete Cosine Transform (DCT) coefficients for palmprint authentication. The score level fusion of hand shape and palmprint features using product rule achieves best performance as compared to Max or Sum rule. However the decisions from the Sum, Max, and Product rules can also be combined to further enhance the performance. Thus the fusion of score level decisions, from the multiple strategies, is proposed and investigated. The two hand shapes of an individual are anatomically similar. However, the palmprints from two hands can be combined to further improve performance and is demonstrated in this paper.

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Combining Multiple Biometric Traits Using Asymmetric Aggregation Operators for Improved Person Recognition

Symmetry ◽

10.3390/sym12030444 ◽

2020 ◽

Vol 12 (3) ◽

pp. 444

Author(s):

Abderrahmane Herbadji ◽

Zahid Akhtar ◽

Kamran Siddique ◽

Noubeil Guermat ◽

Lahcene Ziet ◽

...

Keyword(s):

User Authentication ◽

Recognition System ◽

Error Rates ◽

Aggregation Operators ◽

Border Crossing ◽

Smart Device ◽

Sources Of Information ◽

Score Level Fusion ◽

Mobile Databases ◽

Level Fusion

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 alleviate the error rates and some inherent weaknesses of unibiometrics systems. Therefore, we present, in this study, a novel score level fusion-based scheme for multibiometric user recognition system. The proposed framework is hinged on Asymmetric Aggregation Operators (Asym-AOs). In particular, Asym-AOs are estimated via the generator functions of triangular norms (t-norms). The extensive set of experiments using seven publicly available benchmark databases, namely, National Institute of Standards and Technology (NIST)-Face, NIST-Multimodal, IIT Delhi Palmprint V1, IIT Delhi Ear, Hong Kong PolyU Contactless Hand Dorsal Images, Mobile Biometry (MOBIO) face, and Visible light mobile Ocular Biometric (VISOB) iPhone Day Light Ocular Mobile databases have been reported to show efficacy of the proposed scheme. The experimental results demonstrate that Asym-AOs based score fusion schemes not only are able to increase authentication rates compared to existing score level fusion methods (e.g., min, max, t-norms, symmetric-sum) but also is computationally fast.

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Enhance the classification and Score level Fusion Multi-model Biometric System Based on Fingerprint and Speech Recognition

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i6.956962 ◽

2019 ◽

Vol 7 (6) ◽

pp. 956-962

Author(s):

Shubhleen Sharma ◽

Dinesh Kumar

Keyword(s):

Speech Recognition ◽

Biometric System ◽

Score Level Fusion ◽

Level Fusion

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MFCIS: an automatic leaf-based identification pipeline for plant cultivars using deep learning and persistent homology

Horticulture Research ◽

10.1038/s41438-021-00608-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Yanping Zhang ◽

Jing Peng ◽

Xiaohui Yuan ◽

Lisi Zhang ◽

Dongzi Zhu ◽

...

Keyword(s):

Prunus Avium ◽

Cultivar Identification ◽

Leaf Shape ◽

Persistent Homology ◽

Growth Period ◽

Image Features ◽

Crop Species ◽

Score Level Fusion ◽

Level Fusion ◽

Leaf Image

AbstractRecognizing plant cultivars reliably and efficiently can benefit plant breeders in terms of property rights protection and innovation of germplasm resources. Although leaf image-based methods have been widely adopted in plant species identification, they seldom have been applied in cultivar identification due to the high similarity of leaves among cultivars. Here, we propose an automatic leaf image-based cultivar identification pipeline called MFCIS (Multi-feature Combined Cultivar Identification System), which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network (CNN). Persistent homology, a multiscale and robust method, was employed to extract the topological signatures of leaf shape, texture, and venation details. A CNN-based algorithm, the Xception network, was fine-tuned for extracting high-level leaf image features. For fruit species, we benchmarked the MFCIS pipeline on a sweet cherry (Prunus avium L.) leaf dataset with >5000 leaf images from 88 varieties or unreleased selections and achieved a mean accuracy of 83.52%. For annual crop species, we applied the MFCIS pipeline to a soybean (Glycine max L. Merr.) leaf dataset with 5000 leaf images of 100 cultivars or elite breeding lines collected at five growth periods. The identification models for each growth period were trained independently, and their results were combined using a score-level fusion strategy. The classification accuracy after score-level fusion was 91.4%, which is much higher than the accuracy when utilizing each growth period independently or mixing all growth periods. To facilitate the adoption of the proposed pipelines, we constructed a user-friendly web service, which is freely available at http://www.mfcis.online.

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A Framework for Continuous Authentication Based on Touch Dynamics Biometrics for Mobile Banking Applications

Sensors ◽

10.3390/s21124212 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4212

Author(s):

Priscila Morais Argôlo Bonfim Estrela ◽

Robson de Oliveira Albuquerque ◽

Dino Macedo Amaral ◽

William Ferreira Giozza ◽

Rafael Timóteo de Sousa Júnior

Keyword(s):

User Authentication ◽

Internet Banking ◽

Supervised Machine Learning ◽

Smart Devices ◽

Static Verification ◽

User Behaviors ◽

Legitimate User ◽

Continuous User ◽

Mobile Banking Applications ◽

Effective Layer

As smart devices have become commonly used to access internet banking applications, these devices constitute appealing targets for fraudsters. Impersonation attacks are an essential concern for internet banking providers. Therefore, user authentication countermeasures based on biometrics, whether physiological or behavioral, have been developed, including those based on touch dynamics biometrics. These measures take into account the unique behavior of a person when interacting with touchscreen devices, thus hindering identitification fraud because it is hard to impersonate natural user behaviors. Behavioral biometric measures also balance security and usability because they are important for human interfaces, thus requiring a measurement process that may be transparent to the user. This paper proposes an improvement to Biotouch, a supervised Machine Learning-based framework for continuous user authentication. The contributions of the proposal comprise the utilization of multiple scopes to create more resilient reasoning models and their respective datasets for the improved Biotouch framework. Another contribution highlighted is the testing of these models to evaluate the imposter False Acceptance Error (FAR). This proposal also improves the flow of data and computation within the improved framework. An evaluation of the multiple scope model proposed provides results between 90.68% and 97.05% for the harmonic mean between recall and precision (F1 Score). The percentages of unduly authenticated imposters and errors of legitimate user rejection (Equal Error Rate (EER)) are between 9.85% and 1.88% for static verification, login, user dynamics, and post-login. These results indicate the feasibility of the continuous multiple-scope authentication framework proposed as an effective layer of security for banking applications, eventually operating jointly with conventional measures such as password-based authentication.

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Multimodal score level fusion for recognition using face and palmprint

International Journal of Electrical Engineering Education ◽

10.1177/0020720920929662 ◽

2020 ◽

pp. 002072092092966

Author(s):

Milind E Rane ◽

Umesh S Bhadade

Keyword(s):

Recognition System ◽

Acceptance Rate ◽

Face Database ◽

Biometric Recognition ◽

False Acceptance Rate ◽

Score Level Fusion ◽

Score Fusion ◽

Matching Score ◽

Level Fusion ◽

Fusion Approach

The paper proposes a t-norm-based matching score fusion approach for a multimodal heterogenous biometric recognition system. Two trait-based multimodal recognition system is developed by using biometrics traits like palmprint and face. First, palmprint and face are pre-processed, extracted features and calculated matching score of each trait using correlation coefficient and combine matching scores using t-norm based score level fusion. Face database like Face 94, Face 95, Face 96, FERET, FRGC and palmprint database like IITD are operated for training and testing of algorithm. The results of experimentation show that the proposed algorithm provides the Genuine Acceptance Rate (GAR) of 99.7% at False Acceptance Rate (FAR) of 0.1% and GAR of 99.2% at FAR of 0.01% significantly improves the accuracy of a biometric recognition system. The proposed algorithm provides the 0.53% more accuracy at FAR of 0.1% and 2.77% more accuracy at FAR of 0.01%, when compared to existing works.

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