Fingerprint and Iris Template Protection for Health Information System Access and Security

Abdul, Wadood; Alzamil, Abdullah; Masri, Hammam; Qazi, Emad-ul-Haq; Ghouzali, Sanaa; Hussain, Muhammad; AlZuair, Mansour

doi:10.1166/jmihi.2017.2149

Cited by 5 publications

(3 citation statements)

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“…Fingerprint and Iris Template Protection for Health Information System Access and Security [20,21], focus on template database attacks which includes attacks on the integrity of biometric templates by presenting a new approach using both chaos and Hadamard matrices. Although a considerable amount of work was conducted on protecting biometric templates, the proposed approaches in the literature do not satisfy the main requirements of security, performance, diversity and revocability.…”

Section: Related Workmentioning

confidence: 99%

Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems

Baskar

Devi

Ramkumar

et al. 2021

Neural Process Lett

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The problem of forgery detection has been well studied and the forged finger prints produces highly impacting results in the biometric based security systems. There are many algorithms discussed earlier to detect forged finger prints. However, they suffer to achieve higher performance in terms of security. In this paper, a region centric minutiae propagation measure (RCMPM) based approach. First, the finger print image is read and removes the noisy points by applying the multi level Gabor filters. The Gabor filter has been applied in multiple level which helps to remove the noise from finger print image. The enhanced image is converted into number of integral image. The integral images are generated by splitting the image into number of tiny images according to the size of window. From the integral image produced, the island, dot, enclosure, bifurcation features are extracted. Extracted features are framed as feature vector and used to estimate the RCMPM measure. Based on the RCMPM measure, the presence of forged finger print has been identified and the same has been used to identify the region which has been modified. The accuracy of forged print detection has been improved and reduces the false classification ratio.

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Section: Related Workmentioning

confidence: 99%

Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems

Baskar

Devi

Ramkumar

et al. 2021

Neural Process Lett

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“…Biometric can be divided into two main methods. The first is physical biometric which includes face, fingerprint palm and iris recognition [1][2][3][4][5]. The second is known as behavioral biometric, which includes walking pattern, typing pattern and hand-written signature recognition [6].…”

Section: Introductionmentioning

confidence: 99%

Finding a suitable threshold value for an iris-based authentication system

Wangkeeree¹,

Boonkrong²

2019

IJECE

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<span>Authentication is the first line of defense of any information technology systems. One of the popular methods used today is biometric, and iris authentication is gaining popularity. However, the threshold value is deemed to be secure and appropriate has not been thoroughly studied. Threshold is a value that defines the acceptable amount of the correct bits of the image before securely passing the authentication process. Therefore, the main aim of this research was to find a secure and suitable threshold value used in iris authentication system, where iris localization was done by using Circle Hough Transform technique. Iris image databases v.4 from the Chinese Academy of Sciences Institute of Automatic (CASIA) were used in this research. The way to find the appropriate threshold was to test for the right balance of the GAR, FRR and FAR values when trying to verify the person’s identity. The results of the test revealed that the appropriate threshold had the value of 72.9246 percent of all the available bits of the iris image. Both had a high GAR and very low FAR and FRR values. It can be concluded that the obtained threshold value was suitable and secure.</span>

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“…The rapid development of information technology, the increasing need for information security transmission, and the biometric-related technology are developing rapidly. Human biometrics refer to the information features produced by human tissue structure, including the iris [2][3][4], face [1,5], fingerprint [6][7][8], voice [9][10][11], DNA [12][13][14], and palm postures [15]. The biological characteristics of human body are widely used in identity recognition and other fields because of their uniqueness.…”

Section: Introductionmentioning

confidence: 99%

Research on iris image encryption based on deep learning

Jiang

Chen

et al. 2018

J Image Video Proc.

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With the development of information technology, the demand for information security is increasing. For more convenient and safer needs, the encryption technology based on biometrics has developed rapidly. Among them, iris technology has become an important research object of information security research due to the stability of iris characteristics and its difficulty in forgery. In this paper, the iris feature encryption technology based on the iris is studied by using the method of deep learning as the feature classification method and the iris feature as the research object. The simulation experiment is carried out by using the common iris database. The results show that the method can greatly improve the consistency of iris encryption and improve the security of encryption and decryption process.

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Fingerprint and Iris Template Protection for Health Information System Access and Security

Cited by 5 publications

References 0 publications

Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems

Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems

Finding a suitable threshold value for an iris-based authentication system

Research on iris image encryption based on deep learning

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