Principle Component Analysis(PCA)

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    Principle Component

    Analysis(PCA)

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    What is face recognition?

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    DEFINITION

    The ability to recognize people by their facialcharacteristics.

    Computers can conduct facial database searchesand/or perform live, one-to-one or one-to-manyverifications with unprecedented accuracy andsplit-second processing.

    Users can be granted secure access to theircomputer, mobile devices, or for online e-commerce, simply by looking into their Webcamera.

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    Face recognition application

    Security

    - military applications

    Personal information access

    -ATM

    -Home access Improved human machine interaction

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    Drawback

    More number of database image required

    Complexity

    Time delay

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    Proposed system

    PCA:

    Principal component analysis (PCA) creates

    new variables (components) that consist of

    uncorrelated, linear combinations of the

    original variables.

    PCA is used to simplify the data structure.

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    Existing method

    DWT

    The wavelet transform (WT) has gained widespreadacceptance in signal processing and imagecompression.

    DCTA discrete cosine transform (DCT) expresses a

    sequence of finitely many data points in terms of a sumof cosine functions oscillating at different frequencies

    SVDThe singular value decomposition (SVD) is afactorization of areal or complex, matrix with manyuseful applications in signal processing and statics

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    STEPS OF PCA

    Convert the images 2D to 1D Finding mean

    Finding deviation

    Covariance Eigen value/ Eigen vector

    Eigen face

    Euclidian

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    METHOD 1

    2D to 1D

    If i get 20 images means this process going

    to 2D to 1D

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    METHOD 2

    Finding mean value:

    mean means total number of average value

    Example:Add the numbers: 6 + 11 + 7 = 24

    Divide by how manynumbers

    (there are 3 numbers): 24 / 3 = 8The Mean is 8

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    METHOD 3

    Finding deviation

    each pixel value minus mean value is called

    standard deviation

    Matrix-Mean value

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    METHOD 4

    Covariance:

    input is a matrix, multiplication of

    inverse is called covariance.

    A=1/A

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    METHOD 5

    Finding Eigen value and Eigen vector:

    character equation;

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    METHOD 6

    Euclidian

    Calculating thresh hold value

    ifEU

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    QUESTION?

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    END