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    FACE RECOGNITION

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    FACE RECOGNITION

    The face is our primary focus of attention in social intercourse, playing a major role in conveying indentity and emotion.

    Humans can recognize thousands of faces.

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    FACE RECOGNITION

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    FACE DETECTION

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    FACE RECOGNITION BY APPEARANCE (I)

    Appearance-based methods rely heavily on the concept of an image space. A two-dimensional image I (x,y ) or any type of visual data (depth maps, flow fields, etc.) may be viewed as a vector (or point) in a very high dimensional space, often called the image space, where each coordinate of the space corresponds to one pixel value of the original image. In general, a grayscale image with r rows and c columnsdescribes a vector x in a m -dimensional image space, where m = r x c . With this image representation, the image becomes a very high dimensional feature vector, and perhaps the simplest classification approach is a nearest neighbor classifier in the image space

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    FACE RECOGNITION BY APPEARANCE (II)

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    FACE RECOGNITION BY APPEARANCE (III)

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    Face recognition systems can not be directly implementedin such large spaces as the classifiers are slowed down by the great size of the input vector.

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    FACE RECOGNITION BY APPEARANCE (IV)

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    DIMENSIONALITY REDUCTION

    PCA

    112x92=10304 2

    Reducing the dimensionality of the data, we speed up the computations, without losing too much information.

    The most popular technique for dimensionality reduction purposes is Principal Component Analysis (PCA).

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    FACE RECOGNITION USING PCA

    1. Acquire an initial set of face images (the training set)

    2. Calculate the PCA transform of the training set (compute the eigenvectors-eigenfaces-)

    3. Project the training images onto the eigenspace

    TRAINING

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    FACE RECOGNITION USING PCA and Matlab

    %data matrix

    %each column is a face

    X=[x1 x2 x3 x4 x5 x6 x7 x8];

    % PCA

    [V,D,Average] = pc_evectors(X,Nv);

    %projecting the data

    P=V'*Xm;

    TRAINING (matlab code)

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    FACE RECOGNITION USING PCA and Matlab (II)

    1. Project the new image onto theeigenspace

    2. Use a classifier to see which class is nearest to the new face

    TEST (TO RECOGNIZE NEW FACES)

    classifier.m

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