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Related Concept Videos

Introduction to z Scores01:06

Introduction to z Scores

A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores help...
Introduction to z Scores01:05

Introduction to z Scores

A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores help...
Factorial Design02:01

Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
z Scores and Unusual Values01:07

z Scores and Unusual Values

The z score is one of the three measures of relative standing. It describes the location of a value in a dataset relative to the mean. z scores are obtained after the standardization of the values in a dataset. The z score for the mean is 0.
 This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data value...
z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of zero.
Skewness01:06

Skewness

The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency are...

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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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From scores to face templates: a model-based approach.

Pranab Mohanty1, Sudeep Sarkar, Rangachar Kasturi

  • 1Computer Science and Engineering Department, University of South Florida, FL 33620-5399, USA. pkmohant@cse.usf.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|October 16, 2007
PubMed
Summary

This study introduces a new method to reconstruct face templates from match scores, posing significant security risks to biometric systems. The linear approach models face recognition algorithms, enabling template reconstruction and raising privacy concerns.

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Area of Science:

  • Computer Science
  • Biometrics
  • Cybersecurity

Background:

  • Biometric authentication systems rely on template security.
  • Reconstructing templates from match scores presents significant privacy and security risks.
  • Existing methods may not fully capture the vulnerabilities in face recognition algorithms.

Purpose of the Study:

  • To propose a novel linear approach for reconstructing face templates from match scores.
  • To model the behavior of face recognition algorithms using affine transformations.
  • To assess the security and privacy implications of template reconstruction in biometric systems.

Main Methods:

  • Modeling face recognition algorithms with affine transformations to approximate distance computations.
  • Using an independent image set (break-in) to record match scores against the targeted subject's enrolled template.
  • Embedding the targeted subject in an affine space and reconstructing the original template via inverse transformation.

Main Results:

  • Demonstrated successful template reconstruction across Principal Component Analysis (PCA), Bayesian intra-extrapersonal classifier (BIC), and a commercial algorithm.
  • Achieved high probabilities of breaking into systems (e.g., 73% for commercial, 72% for BIC, 100% for PCA) with limited attempts.
  • Showcased robustness to score quantization and a higher probability of success (47%) compared to hill climbing attacks.

Conclusions:

  • The proposed linear template reconstruction method poses a severe vulnerability to biometric authentication systems.
  • Reconstructing actual user face templates raises significant privacy concerns.
  • This approach highlights a more critical threat than incremental attacks, emphasizing the need for enhanced biometric security measures.