Biometric retinal authentication based on multi– resolution feature extraction using mahalanobis distance

Asem, Morteza Modarresi

doi:10.15406/bbij.2018.07.00188

Cited by 2 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A robust three stage person identification system using human retina is proposed by Modarresi et al [27]. Preprocessing is the first stage and it includes rotation compensation, OD localization, and ROI identification.…”

Section: B Non-vascular Based Methodsmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Elangovan

Nath

2019

View full text Add to dashboard Cite

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%.

show abstract

Section: B Non-vascular Based Methodsmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Elangovan

Nath

2019

View full text Add to dashboard Cite

show abstract

“…Every individual has a unique, stable and impossible-to-replicate retinal pattern. Blood vessel patterns in the retina are not entirely inherited; for example, twins do not have the same blood vessel pattern and are less susceptible to external interference than the other biometrics 4 , 5 . Retinal biometrics are used in physical access entry applications requiring high security.…”

Section: Introductionmentioning

confidence: 99%

“…Blood vessel patterns in the retina are not entirely inherited; for example, twins do not have the same blood vessel pattern and are less susceptible to external interference than the other biometrics. 4,5 Retinal biometrics are used in physical access entry applications requiring high security. It also includes research and development facilities, military bases, and nuclear power plants.…”

Section: Introductionmentioning

confidence: 99%

Human retinal biometric recognition system based on multiple feature extraction

Marappan

Murugesan²,

Malar

et al. 2023

J. Electron. Imag.

View full text Add to dashboard Cite

Retinal biometric trait is one of the safest and most secure biometric traits that can be used for personal identification and authentication. The poor contrast in captured retinal images initially requires the adoption of a preprocessing technique, especially when the vessel segmentation is required. As a result, an effective preprocessing technique is employed before the feature extraction process. The reliability of the segmentation depends on the consistency of the contrast across the images. Due to the complexity of the retinal vascular patterns, segmenting the vessels manually is a time-consuming task that also requires extensive training and abilities to distinguish the thinner blood vessel pixels from the background of the retinal images. We present a unique blood vessel segmentation technique based on Radial Chebyshev Moments (RCMs) shape descriptors. Moment-based shape descriptors are typically strong, concise, and simple to compute. They are also insensitive to the object's scale, rotation, and translation. Furthermore, to eliminate the eye movement and to overcome the various lighting problems, the following multiple features are extracted in the feature extraction stage to accurately authenticate a person: statistical (gray-level) features, shape-based (RCM) features, and structural features, such as minutiae, bifurcation points, optical disc, and enclosed regions. The principal components analysis methodology is used to optimize the feature vector, and the target image is identified using the support vector machine classifier. This step helps to determine whether the test image belongs to the authorized person or not. RIDB and VARIA are the two standard retinal authentication databases and DRIVE and STARE are the two general retinal databases that are used for evaluating the performance of the system. The system yielded a high identification rate of 98%, 97.5%, 97.2% and 94.4% with average processing time of 0.987 s, 0.40 s, 1.262 s and 0.65 s, respectively. From the results, it is evident that the proposed method is more effective and efficient for authenticating individuals.

show abstract

Biometric retinal authentication based on multi– resolution feature extraction using mahalanobis distance

Cited by 2 publications

References 45 publications

International Journal of Electronics and Telecommunications

International Journal of Electronics and Telecommunications

Human retinal biometric recognition system based on multiple feature extraction

Contact Info

Product

Resources

About