authentication

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authentication

Biometrics
The use of a personal feature—e.g., face, hand, fingerprint, signature, voice, iris, or other highly specific indicator—to verify a person’s identity, and restrict access to private information to authorised persons.

Informatics
Confirmation of a user's identity, generally through a user name and password.

authentication

Biometrics The use of a personal feature, eg, face, hand, fingerprint, signature, voice, iris, or other highly specific indicator to verify a person's identity, and restrict access to information to authorized persons; confirmation of a user's identity, generally through user name and password or biometric characteristics. See Biometrics. Cf Authorization.

au·then·ti·ca·tion

(aw-thenti-kāshŭn)
Protocol to confirm the identify someone logged onto a computer.

authentication

(o-then″ti-kā′shŏn)
In an electronic health record or other computer system, a security mechanism (such as a digital signature) that provides for a unique means of identifying a system user.
References in periodicals archive ?
This could help on performance the proposed copy-move forgery detection methods in the aspect of time complexity [13].
Farid, "Image forgery detection," IEEE Signal processing magazine, vol.
[3] Dijana Tralic, Iran Zupancic, Sonja Grgic and Mislar Grgic, "CoMoFoD: New database for copy-move forgery detection," proceedings in 55th International Symposium (ELMAR) 2013, pp.
Shaji, "A study on segmentation-based copy-move forgery detection using DAISY descriptor," Advances in Intelligent Systems and Computing, vol.
Figure 6(c) illustrate the result of SIFT, SURF, and sORB based forgery detection methods in the literature.
1955) (delineating history of handwriting analysis and forgery detection as far back as Roman era), Faigman, supra note 4, at 115 (recognizing long history of handwriting expertise), Andre A.
A generalized scheme for block-based copy-move forgery detection algorithms is summarized in Fig.
Generally, copy-move forgeries detection using block based techniques requires 7 steps [4]; the steps go from dividing the input image into overlapping blocks then calculate features of blocks and final steps are comparing blocks for forgery detection. From [1], a combination between Zernike moments and Wavelet transform was applied to reduce the running time while keeping precision of the original algorithm.
By default, all of these retrieved images (for instance, at most by establishing a threshold on the number of page rank; see Section 4 for a specific threshold setting) could be passed to the successive step of forgery detection and localization, but, to reduce the amount of comparisons to be done within the second phase of the procedure, a selection functionality to skim the raw results has been envisaged and various solutions are still under analysis.
Shah, "Digital image forgery detection using artificial neural network and auto regressive coefficients," in Proceedings of the Canadian Conference on Electrical and Computer Engineering (CCECE '06), pp.
It is urgent to propose effective copy-move forgery detection methods to detect and locate the tampered regions for digital images.
Kim, "Data Forgery Detection for Vehicle Black Box," in Proc.