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Especially, secure Hamming distance can be applied in privacy-preserving biometrics to measure the similarity of two biometric feature vectors on encrypted data.
But you have to keep in mind that biometric systems are not that much more secure than long passwords and if your biometric feature is stolen or lost you can't get a new one.
Each biometric feature has its strengths and weaknesses, and the choice depends on the application.
The above research results provide noteworthy clues for biometric feature acquisition.
At authentication another biometric feature vector Y is obtained and G(W, Y) is calculated.
A biometric feature cannot be forgotten (like a password) or lost (like a token).
In this section, we introduce intrusion attacks via reconstruction of the biometric feature vector from biohashes.
A new biohash created from the estimated biometric feature vector was used to perform imposter attacks.
In particular, the integrated subsystems work on the same biometric feature, the face in this case, yet exploiting different classifiers.
In contrast to the shielding functions, generic quantization schemes define intervals for each single biometric feature based on its variance.
ϕ a, l) denotes a single biometric feature ϕ acquired during the l-th login attempt of user a.
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CEO of Professional Science Editing for Scientists @ prosciediting.com