Publication

Hill-Climbing Attack to an Eigenface-Based Face Verification System

2009
Conference Papers

Abstract

We use a general hill-climbing attack algorithm based on Bayesian adaption to test the vulnerability of an Eigenface-based approach for face recognition against indirect attacks. The attacking technique uses the scores provided by the matcher to adapt a global distribution, computed from a development set of users, to the local specificities of the client being attacked. The proposed attack is evaluated on an Eigenfacebased verification system using the XM2VTS database. The results show a very high efficiency of the hill-climbing algorithm, which successfully bypassed the system for over 85% of the attacked accounts.

Official source

https://infoscience.epfl.ch/entities/publication/93750541-1bec-46d6-92a3-2f2a1b245fc3

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2009
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/93750541-1bec-46d6-92a3-2f2a1b245fc3

About this result

Related concepts (32)

Facial recognition system

A facial recognition system is a technology potentially capable of matching a human face from a or a video frame against a database of faces. Such a system is typically employed to authenticate users through ID verification services, and works by pinpointing and measuring facial features from a given image. Development began on similar systems in the 1960s, beginning as a form of computer application. Since their inception, facial recognition systems have seen wider uses in recent times on smartphones and in other forms of technology, such as robotics.

Genetic algorithm

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, causal inference, etc.

Eigenface

An eigenface (ˈaɪgənˌfeɪs) is the name given to a set of eigenvectors when used in the computer vision problem of human face recognition. The approach of using eigenfaces for recognition was developed by Sirovich and Kirby and used by Matthew Turk and Alex Pentland in face classification. The eigenvectors are derived from the covariance matrix of the probability distribution over the high-dimensional vector space of face images. The eigenfaces themselves form a basis set of all images used to construct the covariance matrix.

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Hill-Climbing Attack to an Eigenface-Based Face Verification System

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