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Acquisition and Computation for Data in Biometric System
Published in Karm Veer Arya, Robin Singh Bhadoria, The Biometric Computing, 2019
In present day applications, identification of a person reliably and conveniently is a major challenge. This is mainly due to explosive growth in internet connectivity and human mobility. Thus a number of different biometric methods including face, fingerprint and multimodal methods have been developed. Fingerprint recognition is used to identify a person by matching fingerprint with that of the fingerprint stored in the database. It has very high accuracy, most economical biometric, most developed biometrics, easy to use and standardized. But it has a disadvantage that the accuracy reduces due to with the dryness in fingers or dirty skin of the finger, it also reduces due to changes in fingerprints with the age and large memory requirement.
Learning Representations for Unconstrained Fingerprint Recognition
Published in Mayank Vatsa, Richa Singh, Angshul Majumdar, Deep Learning in Biometrics, 2018
Aakarsh Malhotra, Anush Sankaran, Mayank Vatsa, Richa Singh
Further, spurious minutia are removed by taking a mean of the probabilities for each minutia location and keeping only those which are greater than 0.5. Such an approach is able to remove multiple spurious minutiae in nearby locations, which are generated because of overlapping patches. Using an in-house data set of 200 labeled fingerprint images, the authors reported the highest accuracy of 92.86% on 63b patches compared to 90.68%, 91.79%, and 91.76% in 45, 45b, and 63 patches, respectively. These results signify that a larger patch size with exterior region blurred is suitable for minutia detection using a deep CNN.
Dynamic Intrinsic Chip ID for Hardware Security
Published in Tomasz Wojcicki, Krzysztof Iniewski, VLSI: Circuits for Emerging Applications, 2017
Toshiaki Kirihata, Sami Rosenblatt
Fingerprints are widely used for secure identification of individuals. A human fingerprint is a unique and unclonable feature that each person possesses. In a similar fashion, a secure intrinsic ID exploits intrinsic features of a VLSI chip. Such features arise from random process variations, and can be used to generate an ID that cannot be reverse-engineered or easily emulated, also called a “Physically Unclonable Function (PUF)” [12–37]. This thus greatly improves chip security over the existing extrinsic ID approach. In this section, we discuss the intrinsic ID generation and authentication concept using random process variations in manufacturing, and their challenges.
Rain Drop Service and Biometric Verification Based Blockchain Technology for Securing the Bank Transactions from Cyber Crimes Using Weighted Fair Blockchain (WFB) Algorithm
Published in Cybernetics and Systems, 2023
RDS is generated for verifying the identity of the user to make the transaction without any third-party approval. After access is granted by verifying the RDS, users will be directly to submit the transaction details. If the transaction is denied, then the generated wallets become invalid to make further transactions. Biometric authentication, such as fingerprint verification or facial recognition is the two primary biometrics modalities that are widely used. According to the survey, Hackers and cyber criminals are well aware of evolving digital technology and have been able to bypass security systems to commit data breaches and fraud in banking transactions. Fingerprint biometric has high accuracy and cost effectiveness when compared to other biometric technology. The comparison of Graphical representation of different techniques is shown in Table 1 and Figure 3.
Proxy re-encryption architect for storing and sharing of cloud contents
Published in International Journal of Parallel, Emergent and Distributed Systems, 2020
None of the methods guarantees complete security, a combination of methods is preferable [33].Face preprocessing: identification based via face. It comprises three steps: detection, registration, and normalization. It recognizes users based on their face. Very accurate, but requires depth camera for safe recognition.Gesture recognition: based on the assumption that each user has unique gesture or movements. Requires devices that can record this type of data.Fingerprint recognition recognizes a user based on their fingerprints. It is one of the most common ways of biometric recognition. Modern devices are often providing fingerprints readers.Voice recognition: recognizes voice patterns. Currently, it is not effective.Palm recognition: Similar to fingerprint recognition, requires devices to read the whole palm.Gesture control recognition: A user can set up the pattern of how the cursor is moving on the screen and uses it as a password [33].
Design of Multiple Share Creation with Optimal Signcryption based Secure Biometric Authentication System for Cloud Environment
Published in International Journal of Computers and Applications, 2022
D. Prabhu, S. Vijay Bhanu, S. Suthir
Rajeswari et al. [17] proposed a novel concept for biometric security schemes depending on fingerprint detection. It automates the authentication approach for matching between 2 individual fingerprints, in which fingerprint is deliberated as a generally employed biometrics for identifying a person and verifying their identification. The clients are validated according to this fingerprint template that should be given around an arbitrary number that was created every time. The problem with this method was that in the digital age information permits the distribution and replication of data through the network. Through this process, similar data can be shared, circulated, and stored multiple times.