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Compatible for any keystrokes, macros let you control Internet Explorer, Google Chrome, Firefox, Safari and a host of other Internet browsers. Add, execute and synchronize your own custom keystrokes.

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Compatible for any keystrokes, macros let you control Internet Explorer, Google Chrome, Firefox, Safari and a host of other Internet browsers. Add, execute and synchronize your own custom keystrokes.

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Compatible for any keystrokes, macros let you control Internet Explorer, Google Chrome, Firefox, Safari and a host of other Internet browsers. Add, execute and synchronize your own custom keystrokes.

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Compatible for any keystrokes, macros let you control Internet Explorer, Google Chrome, Firefox, Safari and a host of other Internet browsers. Add, execute and synchronize your own custom keystrokes.

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Compatible for any keystrokes, macros let you control Internet Explorer, Google Chrome, Firefox, Safari and a host of other Internet browsers. Add, execute and synchronize your own custom keystrokes.

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Compatible for any keystrokes, macros let you control Internet Explorer, Google Chrome, Firefox, Safari and a host of other Internet browsers. Add, execute and synchronize your own custom keystrokes.

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Source code for face classification. Contains no programming required.

License:

Freeware

File Size:

4768
KB

Price:

Free

Last Updated:

2011-08-15

Expert Rating

3.2

OVERVIEW

FisherFaces for Face Matching allows you to create and modify faces in 3D linear subspace.

We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space-if the face is a Lambertian surface without shadowing.

However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher’s Linear Discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions.

Our algorithm is based on the observation that faces in the same class lie in a 3D linear subspace of the high dimensional image space and that the average distance between members of this subspace is much less than the average distance between members of subspaces corresponding to other classes. These facts are used in a pattern classification technique to arrive at a linear discriminant which can be used to perform face recognition. We extend this idea to a representation of faces in a low-dimensional linear subspace and demonstrate that this subspace, from which these faces are projected, corresponds to a linear subspace of the high-dimensional image space in which the average distance between members of the same class is much smaller than the average distance between members of other classes. We show that the 3D face dataset from the MIT Face Database is projected into such a subspace and give the corresponding subspace dimensions for each of the classes in this dataset.

The Fisherface algorithm is compared to the Eigenface algorithm in the recent literature, where both are shown to work well in creating a face image space on which classifying images can be done. In addition, we report some of the

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