face_detection Face Detection using AdaBoost - source code download

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face_detection

Classification:Numerical Algorithm-Artificial Intelligence - matlab Development Tool:matlab Sise:209 KB Upload time:2011/6/10 10:30:38 Uploader:kianoosh111 Download Statistics:

Description

Face Detection using AdaBoost

File list:
face_detection
.............\inputfile
.............\.........\face.JPG

.............\.........\family.jpg

.............\.........\Thumbs.db
.............\.........\women.jpg

.............\sampleset
.............\.........\sampleset
.............\.........\.........\1.JPG

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.............\.........\.........\9.JPG

.............\.........\.........\Thumbs.db
.............\.........\1.JPG

.............\.........\10.JPG

.............\.........\11.JPG

.............\.........\12.JPG

.............\.........\13.jpg

.............\.........\2.JPG

.............\.........\3.JPG

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.............\.........\7.JPG

.............\.........\8.JPG

.............\.........\9.JPG

.............\.........\sampleset.rar
.............\.........\Thumbs.db
.............\face_detection.asv
.............\face_detection.m
.............\get_crcb.m
.............\get_likelyhood.m
.............\make_model.m
.............\plot_hist2d.m
.............\segment_adaptive.m
.............\template.jpg

.............\Thumbs.db

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Related source code

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[FLD Based Face Recognition] - This package implements a well-known FLD-based face recognition method, which is called 'Fisherface' [1]. All functions are easy to use, as they are heavy commented. Furtheremore, a sample script is included to showe their usage. In general, you should follow this order: 1. Select training and test database paths. 2. Select path of the test image. 3. Run 'CreateDatabase' function to create 2D matrix of all training images. 4. Run 'FisherfaceCore' function to produce basis's of facespace. 5. Run 'Recognition' function to get the name of equivalent image in training database. For your convenience, I have prepared sample training and test databases, which are parts of 'face94' Essex face database [2]. You just need to copy the above functions, along with the training and test databases into a specified path (for example 'work' path of your MATLAB root). Then follow dialog boxes, which will appear upon running 'example.m'.
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