Signature-Based Biometric Authentication

Pal, Srikanta; Pal, Umapada; Blumenstein, Michael

doi:10.1007/978-3-319-05885-6_13

Cited by 6 publications

(4 citation statements)

References 69 publications

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“…In identification via signature, the digital form of the signatures of individuals is used. Typically, there are two methods: static method and dynamic method [23]. In static authentication, similarities between a signature, the image of which is recorded with the help of a scanning device, and signatures of the individual stored in the database are detected using various pattern detection methods [24].…”

Section: Behavioural Biometricsmentioning

confidence: 99%

Real-Time Identification from Gait Features Using Cascade Voting Method

Ercin

Karacı

2021

Applied Computer Systems

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There are several biometric methods for identification. These are generally classified under two main groups as physiological and behavioural biometric methods. Recently, methods using behavioural biometric features have gained popularity. Identification made using gait pattern is also one of these methods. The present study proposes a machine learning based system performing identification in real time via gait features using a Kinect device. The data set is composed of 23 individuals’ skeleton model data obtained by the authors. From these data, 147 handcrafted features have been extracted. Deep Neural Network (DNN), Random Forest (RF), Gradient Boosting (GB), XG-Boost (XGB) and K-Nearest Neighbour (KNN) classifiers have been trained with these features. Furthermore, the output of these five machine learning models has been combined with a voting approach. The highest classification has been obtained with 97.5 % accuracy via a voting approach. The classification accuracies of the RF, DNN, XGB, GB and KNN classifiers are 95 %, 87.5 %, 85 %, 80 % and 65 %, respectively. The classification accuracy obtained via a voting approach is higher than in the previous studies. The developed system successfully performs real-time identification.

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Section: Behavioural Biometricsmentioning

confidence: 99%

Real-Time Identification from Gait Features Using Cascade Voting Method

Ercin

Karacı

2021

Applied Computer Systems

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“…T HERE is a huge demand for stable, reliable, and authentic person identification system as the world is manifested to threats and insecurities due to advancements in technologies. Person can be identified based on PIN (license number), biographical attributes (address, education, profession) and biological attributes [1][2][3]. Out of them biometrics based recognition provides better security and safety compared to the traditional methods of recognition.…”

Section: Introductionmentioning

confidence: 99%

“…Identification of an individual by using their signature is termed as signature recognition [1]. Signature is a behavioural trait and may change over time and thus does not provide longterm reliability.…”

Section: Introductionmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Elangovan

Nath

2019

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In this paper a review on biometric person identification has been discussed using features from retinal fundus image. Retina recognition is claimed to be the best person identification method among the biometric recognition systems as the retina is practically impossible to forge. It is found to be most stable, reliable and most secure among all other biometric systems. Retina inherits the property of uniqueness and stability. The features used in the recognition process are either blood vessel features or non-blood vessel features. But the vascular pattern is the most prominent feature utilized by most of the researchers for retina based person identification. Processes involved in this authentication system include pre-processing, feature extraction and feature matching. Bifurcation and crossover points are widely used features among the blood vessel features. Non-blood vessel features include luminance, contrast, and corner points etc. This paper summarizes and compares the different retina based authentication system. Researchers have used publicly available databases such as DRIVE, STARE, VARIA, RIDB, ARIA, AFIO, DRIDB, and SiMES for testing their methods. Various quantitative measures such as accuracy, recognition rate, false rejection rate, false acceptance rate, and equal error rate are used to evaluate the performance of different algorithms. DRIVE database provides 100% recognition for most of the methods. Rest of the database the accuracy of recognition is more than 90%.

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“…The feature extraction is used as an input to the process of learning and decision-making process. Therefore the feature extraction technique is critical to the success of the whole process automatic pattern identification [10]. A useful feature is a feature that allows the system to identify the pattern class with the least amount of mistakes.…”

Section: Introductionmentioning

confidence: 99%

Component-connected Feature for Signature Identification

Umniati

Mutiara

Kusuma

et al. 2018

International Journal on Advanced Science, Engineering and Information Technology

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Abstract-A signature is the oldest security techniques to verify the identification of a person. This is due to every person has a different signature, and each signature has the characteristic physiological and behavior. There are two kinds of signature such as offline and online signatures used to verify someone identity. Offline signatures were used in this study because offline signature does not have dynamic features such as an online signature. This study proposed an identification system of offline signature by using k-NN based on the features that were stored in the database. The proposed identification system consists of preprocessing, feature extraction and verification stages. We collected the data samples from 10 persons. Each person wrote ten signatures. Total data was 100 signatures. The first stage used in this study was preprocessing such as noise removal, binarization, skeleton, and cropping. The second stage was feature extraction. Feature extraction had some vital information such as height-width ratio, the ratio of the density of signatures, edge distance ratio, the ratio of the number and proximity of the column, and the number of connected components in the signature. That information was stored in a separate database. We separated ten signatures of each person into six signatures as data sample and four signature as test data. We verified 40 signatures of test data from 10 persons using k-NN. It is shown that from 40 signatures used in our test data, 28 signatures were correctly identified and 12 signatures belong to others.

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Signature-Based Biometric Authentication

Cited by 6 publications

References 69 publications

Real-Time Identification from Gait Features Using Cascade Voting Method

Real-Time Identification from Gait Features Using Cascade Voting Method

International Journal of Electronics and Telecommunications

Component-connected Feature for Signature Identification

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