An enhanced architecture for serial fusion based multi-biometric verification system - Hossain, Shafaeat; Chen, Jundong; Rahman, Khandaker Abir (International Symposium on Networks, Computers and Communications, 2018)
The traditional architecture of serial fusion based multi-biometric verification systems places an average performing or the worst performing individual verifier in the final stage. Because the final stage gives the verification decision using a single threshold and takes on the most confusing samples which are rejected by all previous stages, an average or the worst performing individual verifier may incur high verification errors in the final stage, which may negatively impact the performance of the whole system. Unfortunately, it is not possible to place a strong individual verifier in the final stage of a traditional architecture because if we place a strong individual verifier in the final stage, we will have to place a weak individual verifier in an earlier stage. Studies show that placing a weak individual verifier in an earlier stage worsens the performance of the whole system by giving more wrong decision earlier. Hence, the challenge is-how can we place the best performing individual verifier in the first stage and at the same time not place an average or the worst performing individual verifier in the final stage? In this paper, we address this challenge. We have come up with a very simple but effective solution. We have proposed a modification to the traditional architecture of serial fusion based multi-biometric verification systems. With rigorous experiments on the NIST multi-modal dataset and using three serial fusion based multi-biometric verification schemes, we demonstrated that our proposed architecture significantly improves the performance of serial fusion based multi-biometric verification systems. © 2018 IEEE.
1 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Architecture; Biometrics; Effective solution; Multi biometrics; Multi-modal dataset; Network architecture; Proposed architectures; Serial fusion; Traditional architecture; dept.csc

An empirical study on verifier order selection in serial fusion based multi-biometric verification system - Hossain, Md Shafaeat; Rahman, Khandaker Abir (Advances in Artificial Intelligence: From Theory to Practice, 2017)
Selecting the order of verifier in a serial fusion based multi-biometric system is a crucial parameter to fix because of its high impact on verification errors. A wrong choice of verifier order might lead to tremendous user inconvenience by denying a large number of genuine users and might cause severe security breach by accepting impostors frequently. Unfortunately, this design issue has been poorly investigated in multi-biometric literature. In this paper, we address this design issue by performing experiments using three different serial fusion based multi-biometric verification schemes, in particular (1) symmetric scheme, (2) SPRT-based scheme, and (3) Marcialis et al.’s scheme. We experimented on publicly available NIST-BSSR1 multi-modal database. We tested 24 orders—all possible orders originated from four individual verifiers—on a four-stage biometric verification system. Our experimental results show that the verifier order “best-to-worst”, where the best performing individual verifier is placed in the first stage, the next best performing individual verifier is placed in the second stage, and so on, is the top performing order for all three serial fusion schemes mentioned above.
2 citations (Crossref) [2023-10-31] Citation Key Alias: hossainEmpiricalStudyVerifier2017, pop00277 tex.type: [object Object]
Keywords: Biometric verification; Biometrics; Crucial parameters; Intelligent systems; Multi biometrics; Multi-biometric verification; Multibiometric systems; Multimodal database; Order selection; Security breaches; Serial fusion; Verifier order selection; dept.csc

DAAB: Deep authorship attribution in bengali - Dipongkor, Atish Kumar; Islam, Saiful; Kayesh, Humayun; Hossain, Shafaeat; Anwar, Adnan; Rahman, Khandaker Abir; Razzak, Imran (2021 international joint conference on neural networks (IJCNN), 2021)
Authorship attribution identifies the true author of an unknown document. Authorship attribution plays a crucial role in plagiarism detection and blackmailer identification, however, the existing studies on authorship attribution in Bengali are limited. In this paper, we propose an instance-based deep authorship attribution model, called DAAB, to identify authors in Bengali. Our DAAB model fuses features from convolutional neural networks and another set of features from an artificial neural network to learn the stylometry of an author for authorship attribution. Extensive experiments with three real benchmark datasets such as Bengali-Quora and two online Bengali Corpus demonstrate the superiority of our authorship attribution model. © 2021 IEEE.
2 citations (Crossref) [2023-10-31]
Keywords: Authorship attribution; Benchmark datasets; Bengalis; Convolution; Convolutional neural network; Convolutional neural networks; Deep learning; Deep neural networks; Learn+; Plagiarism detection; Sets of features; Stylometry; dept.csc