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Correlation Filters Face Verification for Windows

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Correlation Filters Face Verification Analysis

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Correlation Filters Face Verification is an educational software designed for face verification tasks. This free program operates on the Windows platform and falls under the reference software category. Its primary function is to utilize correlation filters for accurately verifying facial features, making it a valuable resource for research and academic purposes. Users can explore various algorithms and techniques related to face recognition and verification, contributing to a deeper understanding of image processing and machine learning concepts.

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The software offers a user-friendly interface that allows for easy navigation through its features. With a focus on educational applications, it provides insights into the methodologies behind face verification technologies. Users can experiment with different parameters and settings to observe the effects on verification outcomes. Correlation Filters Face Verification serves as a practical tool for students and professionals alike, aiming to enhance their knowledge in the field of facial recognition technology.

Icon of program: Correlation Filters Face …

Correlation Filters Face Verification for Windows

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Used Correlation Filters Face Verification for Windows?

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Full Specifications

GENERAL
Release
Latest update
Version
2.0
OPERATING SYSTEMS
Platform
Windows
Operating System
  • Windows 2003
  • Windows NT
  • Windows 2000
  • Windows 7
  • Windows Server
  • Windows ME
  • Windows Vista
  • Windows 10
  • Windows 8
  • Windows XP
  • Windows 98
Additional Requirements
Matlab
POPULARITY
Total Downloads
108
Downloads Last Week
0
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Program available in other languages

Last Updated

Developer’s Description

By Luigi Rosa
Perform face verification with correlation filters using provided Matlab source code.
Correlation filters have been applied successfully to automatic target recognition (ATR) problems. The most basic correlation filter is the matched spatial filter (MSF), whose impulse response (in 2-D, point spread function) is the flipped version of the reference image. While the MSF performs well at detecting a reference image corrupted by additive white noise, it performs poorly when the reference image appears with distortions (e.g., rotations, scale changes). Thus one MSF will be needed to detect each appearance of an object. Clearly this is computationally unattractive for practical pattern recognition. Hester and Casasent addressed this challenge with the introduction of the synthetic discriminant function (SDF) filter. The SDF filter is a linear combination of MSFs where the combination weights are chosen so that the correlation outputs corresponding to the training images would yield pre-specified values at the origin. These pre-specified peak values are often referred to as peak constraints. The peak values corresponding to the authentics (also called the true class) are typically set to 1, and hence this SDF filter was known as the equal correlation peak (ECP) SDF filter. In principle, a single ECP SDF filter could replace many MSFs. Object recognition is performed by cross-correlating an input image with a synthesized template or filter and processing the resulting correlation output. The correlation output is searched for peaks, and the relative heights of these peaks are used to determine whether the object of interest is present or not. The locations of the peaks indicate the position of the objects.

Face verification is an important tool for authentication of an individual and it can be of significant value in security and e-commerce applications. We have developed an effective application of correlation filters for face verification. The performance of a specific type of correlation filter called the minimum average correlation energy (MACE) filter is evaluated using Facial Expression Database collected at the Advanced Multimedia Processing Lab at Carnegie Mellon University (CMU). Index Terms: Matlab, source, code, face, identification, authentication, recognition, correlation, filters, filter, mace.

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AI Assisted Content Disclosure

Content created and reviewed by Softonic with information obtained from Luigi Rosa, using AI.

CNET's editorial team was not involved in the creation of this content. Opinions, analysis and reviews were not provided by CNET.