A Study of Biometric Identification and Verification System

Vandana, .; Kaur, Navdeep

doi:10.1109/icacite51222.2021.9404735

Cited by 13 publications

(4 citation statements)

References 22 publications

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“…The convolution process applies to both the textual data network and the image, capturing similar components within the overall picture. The size of the resulting network without padding is determined by (12).…”

Section: Sequential Feature Score Rank Fusionmentioning

confidence: 99%

“…Data fusion is regarded as a very challenging undertaking due to a number of factors, including, i) the complexity of the data, ii) the fact that the processes depend on n variables without being all measurably dependent, and iii) the difficulty of utilizing the advantages of each set while ignoring the disadvantages in heterogeneous data sets [12]. Random processes, computational intelligence, heuristic optimization, and other methods are utilized in data fusion.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing dual modal biometric authentication: hybrid HPO-ANFIS and HPO-CNN framework

Singh,

Tiwari

2024

IJEECS

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In the realm of secure data access, biometric authentication frameworks are vital. This work proposes a hybrid model, with a 90% confidence interval, that combines "hyperparameter optimization-adaptive neuro-fuzzy inference system (HPO-ANFIS)" parallel and "hyperparameter optimization-convolutional neural network (HPO-CNN)" sequential techniques. This approach addresses challenges in feature selection, hyperparameter optimization (HPO), and classification in dual multimodal biometric authentication. HPO-ANFIS optimizes feature selection, enhancing discriminative abilities, resulting in improved accuracy and reduced false acceptance and rejection rates in the parallel modal architecture. Meanwhile, HPO-CNN focuses on optimizing network designs and parameters in the sequential modal architecture. The hybrid model's 90% confidence interval ensures accurate and statistically significant performance evaluation, enhancing overall system accuracy, precision, recall, F1 score, and specificity. Through rigorous analysis and comparison, the hybrid model surpasses existing approaches across critical criteria, providing an advanced solution for secure and accurate biometric authentication.

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Section: Sequential Feature Score Rank Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing dual modal biometric authentication: hybrid HPO-ANFIS and HPO-CNN framework

Singh,

Tiwari

2024

IJEECS

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“…The FAR and FRR are metric approaches that employ biometric authentication to rate the biometric sensors' ease and the biometric system's security, respectively. These two metrics highlight the compromise between the system's ease and security (Kaur N. , 2021). FAR is a measure used in biometric authentication systems to calculate how frequently the system mistakenly accepts a fraud or unauthorized user as a legitimate user (Eq 4).…”

Section: Evaluation Metricsmentioning

confidence: 99%

Fingerprints to Authenticate Transactions in Contactless Cards

Ibrahim,

Al-Khalil

2023

SJUOZ

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The contactless bank card is a replica of the old fashion payment methods. The contactless card saves the customer a lot of time and effort because the cardholder can tap the card on the card reader instead of carrying a massive amount of cash or memorizing a long password. The transaction will be done in a few seconds, which is a magnificent technique for a very rush and speedy world like this. However, because the contactless card does not require a PIN or signature, it is vulnerable to different types of attacks, and the card can be used by every single person who has the card, even if they are not the real cardholder. Nevertheless, for each new problem, there is a unique solution. Hence this paper presents an innovative way to overcome this problem by embedding a fingerprint sensor into the contactless card to add an extra level of security by creating a virtual environment giving a contactless card and using a minutiae-based algorithm for fingerprint recognition in this contactless card. The work is evaluated based on accuracy using two metrics, false acceptance rate (FAR) and false rejection rate (FRR), algorithm’s matching time, and transaction time. This work shows a good result regarding the transaction time and the possibility of integrating the fingerprint into the contactless card. It displays how fingerprint image quality and features affect fingerprint authentication results. However, it also shows that minutiae-based techniques are not adequate when the dataset is relatively small and has data with low-quality and/or noisy data.

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“…As a research topic in computer vision and pattern recognition that has been conducted for 50 years, has grow rapidly in the recent years and is considered as one of the most successful systems for its convenience, accuracy and has a wide range of applications [1]. In comparison to fingerprint recognition systems which need specific devices to obtain clear fingerprints, or voice verification systems that may suffer from noise and tone changes, face recognition systems are both consistent and easier to obtain inputs from non-ideal environments using inexpensive cameras [2]. It has gradually become an inseparable part of the modern identification system and extensively used in areas such as law enforcement, security, and consumer electronics.…”

Section: Introductionmentioning

confidence: 99%

CNN combined with data augmentation for face recognition on small dataset

Chen

2023

J. Phys.: Conf. Ser.

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Faces have universal structures yet contain distinct features among individuals. Recognizing individuals based on their faces has always been a popular topic in pattern recognition, and computer vision and many traditional approaches have yielded satisfying results. In recent years, rapid growth in deep learning has encouraged researchers to use deep learning methods to solve authentication problems. Convolutional neural networks are one of the most popular deep neural networks with multiple layers and the ability to reduce parameters by using kernels to capture features from input. It has outstanding performance in pattern recognition due to its ability to extract features and take images as inputs. In machine learning, data augmentation is a technique to seemingly enlarge a dataset to avoid underfitting or overfitting problems caused by insufficient data. This paper uses convolutional neural networks to solve face recognition problems on a small dataset. It compares performance with traditional face recognition methods such as Principal Component Analysis and examines the impact on performance using data augmentation. Overall, data augmentation boosts the accuracy of the network but also results in an unsteady learning curve. The convolutional neural network performs well on pattern recognition and obtains an accuracy of 94% in an augmented dataset with only two convolutional layers.

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A Study of Biometric Identification and Verification System

Cited by 13 publications

References 22 publications

Optimizing dual modal biometric authentication: hybrid HPO-ANFIS and HPO-CNN framework

Optimizing dual modal biometric authentication: hybrid HPO-ANFIS and HPO-CNN framework

Fingerprints to Authenticate Transactions in Contactless Cards

CNN combined with data augmentation for face recognition on small dataset

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