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Source code for fingerprint recognition, face recognition and much more

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Face detection continues to be one of the most popular research areas of computer vision and machine learning. Along with the development of face detection algorithms, a comparatively large number of face databases have been collected. Face detection algorithms typically have to be trained on face and nonface images to build up an internal representation of the human face. According to a recent survey of face detection algorithms, popular choices are the FERET, MIT, ORL, Harvard, and AR databases. Along with these public databases, independently collected, nonpublic databases are often also employed. To comparatively evaluate the performance of face detection algorithms, common testing data sets are necessary. These data sets should be representative of real-world data containing faces in various orientations against a complex background.

We have collected a database of 9868 images manually cropped, with different lighting conditions and facial expressions. Facial images are centered, unnormalized, with eyes at fixed positions. The files are in BMP format. The size of each image is 24x24 pixels, with 256 grey levels per pixel.

Extracting face patterns is usually a tedious and time-consuming work that has to be done manually. An interesting alternative is to generate artificial samples for training the classifier. From this comprehensive database it is possible to create a large variety of synthetic face patterns by morphing between different facial images and by modifying the orientation and the illumination.

Index Terms: Matlab, source, code, face, detection, database, image, facial, images, databases, dataset.





Figure 1. Face database

A comprehensive database for face detection algorithms.

Major features


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Face Detection Database. Click here for your donation. In order to obtain the full database you have to pay a little sum of money: 50 EUROS (less than 70 U.S. Dollars).

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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 14 SP1. Matlab is required. 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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