.: Click here to download :.
.: Click here to watch a video tutorial :.
Eigenfaces is the name given to a set of eigenvectors when they are 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. This produces dimension reduction by allowing the smaller set of basis images to
represent the original training images. Classification can be achieved by comparing how faces are represented by the basis set.
We have developed a fast and reliable Python code for face recognition based on Principal Component Analysis (PCA). Proposed algorithm results computationally inexpensive and
it can run also in a low-cost pc such as Raspberry PI.
Requirements: Python 2.7, Numpy, PIL, Tkinter.
Index Terms: Python, face, recognition, PCA, Principal Component Analysis, Raspberry PI.
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Figure 1. Face recognition |
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A simple and effective source code for Python Face Recognition. |
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Release |
Date |
Major features |
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1.0 |
2018.06.27 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. This donation has to be considered an encouragement to improve the code itself. |
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Python Face Recognition - Click here for
your donation. In order to obtain the source code you
have to pay a little sum of money: 30 EUROS (less
than 42 U.S. Dollars). |
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Once you have done this, please email us luigi.rosa@tiscali.it As soon as possible (in a few days) you will receive our new release of Python Face Recognition. Alternatively, you can bestow using our banking coordinates:
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All the code provided is written in Python
language (.py files), with no compiled or other
protected parts of code (executables). The code was
developed with Python 2.7. 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.
