Exact(1)
In this evaluation, we thus concentrate on these two fundamental attack vectors: The Database administrator (DBA) possesses administrator privileges on the database itself, including the ability to change logging routines and user rights, as well as read access to the underlying file system.
Similar(57)
Therefore, choices of parameters in this work are based on preliminary testing and summary statistics from the distance distribution between the query vector and all vectors in the database.
Storing face images (or corresponding feature vectors) in the database in plain form is the most serious security drawback of this system.
We assume that a vector of interest, called "query", is known (either produced by actual measurements, or made up), and we want to find similar vectors in the database.
For each unrelated biometric feature vector from the database, a new feature vector was estimated by minimizing the Euclidean distance between the new feature vector and the unrelated biometric feature vector subject to the consistency criterion (i.e., the new biohash created from the estimated feature vector exactly matches the original biohash).
and store the vector in the database.
For every vector in the database, the range of its values, E max and E min is calculated with step δ = E max -E min /K.
The system computes the Hamming distance between the test biometric hash vector and the claimed user's reference biometric hash vector stored in the database.
The three-by-three rotation matrix and the translation vector to overlay the database conformer D on the reference shape R are merged into a single four-by-four affine transformation matrix (M RD ).
Feature vectors are indexed in the database.
In contrast, neighborhoods defined in terms of k-mer word vectors grow larger as the database grows larger.
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