Fixed-length asymmetric binary hashing for fingerprint verification through GMM-SVM based representations

Topcu, Berkay; Erdoğan, Hakan

doi:10.1016/j.patcog.2018.11.029

Cited by 9 publications

(6 citation statements)

References 25 publications

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“…One of the most outstanding application areas for the SVMs is biometrics [13], [14], [24]- [28]. For example, the SVM-based model has been widely used for face verification and recognition by using Local Binary Patterns (LBP) as feature extractor [26].…”

Section: Black-box Approachmentioning

confidence: 99%

“…For example, the SVM-based model has been widely used for face verification and recognition by using Local Binary Patterns (LBP) as feature extractor [26]. In the same vein, SVM has widely been used as classifier for iris and fingerprint authentication/verification and identification/recognition [13], [14], [27], [28].…”

Section: Black-box Approachmentioning

confidence: 99%

“…The idea is to use a database d 1 for training the model and use this database d 1 as internal validation, and after that, the second database d 2 is used as external validation. This type of validation is widely used in biometrics problems such as iris and fingerprint identification [13], [14], [24]- [28], [164].…”

Section: Measuring the Effectiveness Of The Applied Modelmentioning

confidence: 99%

“…Both white-and black-box approaches have shown accurate results for different practical problems, but usually, when one is suitable for a problem obtaining accurate results, then, another one obtains poor results [4], [8], [11]- [14]. As a consequence, some questions arise:…”

Section: Introductionmentioning

confidence: 99%

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Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View

2019

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Nowadays, in the international scientific community of machine learning, there exists an enormous discussion about the use of black-box models or explainable models; especially in practical problems. On the one hand, a part of the community defends that black-box models are more accurate than explainable models in some contexts, like image preprocessing. On the other hand, there exist another part of the community alleging that explainable models are better than black-box models because they can obtain comparable results and also they can explain these results in a language close to a human expert by using patterns. In this paper, advantages and weaknesses for each approach are shown; taking into account a stateof-the-art review for both approaches, their practical applications, trends, and future challenges. This paper shows that both approaches are suitable for solving practical problems, but experts in machine learning need to understand the input data, the problem to solve, and the best way for showing the output data before applying a machine learning model. Also, we propose some ideas for fusing both, explainable and blackbox, approaches to provide better solutions to experts in real-world domains. Additionally, we show one way to measure the effectiveness of the applied machine learning model by using expert opinions jointly with statistical methods. Throughout this paper, we show the impact of using explainable and black-box models on the security and medical applications. INDEX TERMS Black-box, white-box, explainable artificial intelligence, deep learning.

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Section: Black-box Approachmentioning

confidence: 99%

Section: Black-box Approachmentioning

confidence: 99%

Section: Measuring the Effectiveness Of The Applied Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View

2019

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“…Nowadays, machine learning is becoming popular and effective tool to solve problems in the field of medicine Jaworek-Korjakowska & Tadeusiewicz, 2014;Szaleniec et al, 2013;Szaleniec, Tadeusiewicz, & Witko, 2008;Tadeusiewicz, 2015). It has been used for human authentication (Omara, Emam, Hammad, & Zuo, 2017;Omara, Li, Xiao, Adil, & Zuo, 2018;Topcu & Erdogan, 2019), especially using ECG (Bin Safie et al, 2014;Choudhary & Manikandan, 2015;Gurkan et al, 2013;Guven et al, 2018;Hammad, Luo, & Wang, 2019;Karegar et al, 2017;Louis et al, 2016;Pal & Singh, 2018;Safie et al, 2011;Salloum & Kuo, 2017). The major short comings of these methods (Bin Safie et al, 2014;Choudhary & Manikandan, 2015;Gurkan et al, 2013;Guven et al, 2018;Hammad, Luo, & Wang, 2019;Karegar et al, 2017;Louis et al, 2016;Pal & Singh, 2018;Safie et al, 2011;Salloum & Kuo, 2017) are given below:…”

mentioning

confidence: 99%

ResNet‐Attention model for human authentication using ECG signals

et al. 2020

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Authentication is the process of verifying the claimed identity of the user. Recently, traditional authentication methods such as passwords, tokens, and so on are no longer used for authentication as they are more prone to theft and different types of violations. Therefore, new authentication approaches based on biometric modalities such as heartbeat pattern obtained from electrocardiogram (ECG) signals are considered. Unlike other biometrics, ECG provides the assurance that the person is alive, and is considered as one of the most accurate recent methods for authentication. In this article, two end-to-end deep neural network models for ECG-based authentication are proposed. In the first model, a convolutional neural network (CNN) is developed and in the second model, a residual convolutional neural network (ResNet) with attention mechanism called ResNet-Attention is designed for human authentication. We have used 2-s duration ECG signals obtained from two ECG databases (Physikalisch-Technische Bundesanstalt [PTB] and Check Your Bio-signals Here initiative [CYBHi]) for authentication. Our proposed ResNet-Attention algorithmachieved an accuracy of 98.85 and 99.27% using PTB and CYBHi, respectively. The results obtained by our developed model show that the performance is better than existing algorithms and can be used in real-time authentication systems after the validation with more diverse ECG data.

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Privacy-preserving and verifiable multi-instance iris remote authentication using public auditor

2021

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Fixed-length asymmetric binary hashing for fingerprint verification through GMM-SVM based representations

Cited by 9 publications

References 25 publications

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View

ResNet‐Attention model for human authentication using ECG signals

Privacy-preserving and verifiable multi-instance iris remote authentication using public auditor

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