Verification of Biometric Traits using Deep Learning

doi:10.35940/ijitee.j1083.08810s19

Cited by 2 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Forty images of resolution 768 × 584 pixels have been randomly opted from them to construct the online database. DRIVE database is rotated based on the "Data Augmentation" concept [15] [16]. Rotation angles applied to retinal images are: ±10˚, ±15˚, ±20˚, ±25˚, ±30˚, ±35˚.…”

Section:  Digital Retinal Images For Vessel Extractionmentioning

confidence: 99%

Comprehensive Study and Evaluation of Commonly used Dimensionality Reduction Techniques in Biometrics Field

Sultan¹,

Ghanim²

2020

(AREJ)

View full text Add to dashboard Cite

In biometrics field, usually feature vectors have major length and contain ineffective information. This problem is so called "curse of dimensionality". Hence, there is a need for efficient dimensionality reduction technique to remove the redundant features and reduce the size of feature vectors to get high accuracy rate with fast performance. In this paper a comprehensive study of commonly used dimensionality reduction techniques: Principle Component Analysis, Linear Discremenant Analysis, and Generalized Discremenant Analysis, have been handled. Theoretical background of these techniques is illustrated along with the methods used to calculate their projection spaces then; practical implementation is conducted to find out and adopt the best one for retina based biometric authentication system. From this extensive study, it has been concluded that PCA technique has a number of problems make it has a bad classification power. LDA technique has a number of problems make it impossible to implement in most cases of biometrics field, while GDA technique is more efficient than the PCA and LDA techniques for dimensionality reduction purpose. It has high classification power and consumes less computational time. Hence, GDA technique is adopted in the proposed authentication system.

show abstract

Section:  Digital Retinal Images For Vessel Extractionmentioning

confidence: 99%

Comprehensive Study and Evaluation of Commonly used Dimensionality Reduction Techniques in Biometrics Field

Sultan¹,

Ghanim²

2020

(AREJ)

View full text Add to dashboard Cite

show abstract

“…Research [13] Identify biometric characteristics using deep learning. The deep learning system proposed in this study is MultiTraitConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminatory features from images with 5 classes, namely face, fingerprint, footprint, iris and fingerprint, respectively.…”

Section: Introductionmentioning

confidence: 99%

Retinal biometric identification using convolutional neural network

Rodiah¹,

Madenda

Susetianingtias³

et al. 2021

Computer Optics

View full text Add to dashboard Cite

Authentication is needed to enhance and protect the system from vulnerabilities or weaknesses of the system. There are still many weaknesses in the use of traditional authentication methods such as PINs or passwords, such as being hacked. New methods such as system biometrics are used to deal with this problem. Biometric characteristics using retinal identification are unique and difficult to manipulate compared to other biometric characteristics such as iris or fingerprints because they are located behind the human eye thus they are difficult to reach by normal human vision. This study uses the characteristics of the retinal fundus image blood vessels that have been segmented for its features. The dataset used is sourced from the DRIVE dataset. The preprocessing stage is used to extract its features to produce an image of retinal blood vessel segmentation. The image resulting from the segmentation is carried out with a two-dimensional image transformation such as the process of rotation, enlargement, shifting, cutting, and reversing to increase the quantity of the sample of the retinal blood vessel segmentation image. The results of the image transformation resulted in 189 images divided with the details of the ratio of 80 % or 151 images as training data and 20 % or 38 images as validation data. The process of forming this research model uses the Convolutional Neural Network method. The model built during the training consists of 10 iterations and produces a model accuracy value of 98 %. The results of the model's accuracy value are used for the process of identifying individual retinas in the retinal biometric system.

show abstract

Verification of Biometric Traits using Deep Learning

Cited by 2 publications

References 17 publications

Comprehensive Study and Evaluation of Commonly used Dimensionality Reduction Techniques in Biometrics Field

Comprehensive Study and Evaluation of Commonly used Dimensionality Reduction Techniques in Biometrics Field

Retinal biometric identification using convolutional neural network

Contact Info

Product

Resources

About