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Source code for fingerprint recognition, face recognition and much more

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.: Click here to watch a video tutorial :.

We have developed a fast and optimized algorithm to perform face identification using Minimum Average Correlation Energy (MACE) filtering technique. The performances of the proposed algorithm are evaluated using Facial Expression Database collected at the Advanced Multimedia Processing Lab at Carnegie Mellon University (CMU). Database consists of 13 subjects, each with 75 images. The size of each image is 6464 pixels, with 256 grey levels per pixel. We have achieved an EER equal to 3.50%. All code has been developed in Octave language.

Function list

Function name: [out]=faceverification(filename1,filename2)
Inputs: filename1: complete filename of first image, filename2: complete filename of second image
Ouputs: out: verification result, 1 for matching, 0 for non matching.
Description: This function receives as input filenames of input images and it returns 1 if images match, 0 otherwise.

Function name: [out]=facerecognition(filename)
Inputs: filename: complete filename of input image
Ouputs: out: recognition result, the ID of recognized face.
Description: This function receives as input filename of input image and it returns the ID of recognized image present in database. If input image is not recognized code returns 0. Database has to include at least one image.

Function name: addtodatabase(filename,ID)
Inputs: filename: complete filename of input image, ID: face ID
Ouputs: none
Description: This function adds a facial image to database.

Function name: databaseinfo()
Inputs: none
Ouputs: none
Description: This function shows all facial images present in database. For each image ID and path are shown.

Function name: deletedatabase()
Inputs: none
Ouputs: none
Description: This function removes database from disk.

Other functions are available on request, such as: score visualization, score output, TOP-N face recognition, image enhancement.

Index Terms: Octave, source, code, correlation, filters, automated, face, identification, system.






Figure 1. Correlation color coding for facial image

A simple and effective source code for Optimized Face Identification.

Watch a video tutorial. Octave is required.

Major features


  • One-to-many (1:N) face identification
  • One-to-one (1:1) face verification
  • High recognition rate
  • Fast and optimized implementation
  • Command-line functions
  • Octave language
  • Face database
  • Full compatibility with Matlab language
  • Video tutorial with all supported features

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This donation has to be considered an encouragement to improve the code itself.

Optimized Face Identification - Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 400 EUROS (less than 560 U.S. Dollars).

Once you have done this, please email us
As soon as possible (in a few days) you will receive our new release of Optimized Face Identification.

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Luigi Rosa
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Poste Italiane
Bank address:
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All the code provided is written in Octave language (M-files and/or M-functions), with no dll or other protected parts of code (executables). The code was developed with Octave 3.2.4. The code provided has to be considered "as is" and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.

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