Wavelet Presentation

8/6/2019 Wavelet Presentation

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In the name of

Allah


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Outlines

y Face Recognition system

y Principal Component Analysis

y Wavelet decomposition of an image

y MLP Neural Network

y Proposed Method

y Face Database

y

Experimental Resultsy Conclusion

y Matlab Implemention


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Face Recognition System


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Principal Component Analysis

y Let the training set of images be

y The average face of the set is

defined by

y Each face differs from the averageby vector


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y The co- variance matrix is formed by

y where the matrix

for k=1,.., M', where M'


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Advantage:

y Reduction dimensionality or features

y

It is well known that PCA gives a ver

y goodrepresentation of the faces.

Drawbacks

y

Common PCA-based methods suffer from twolimitations, namely, poordiscriminatory powerand

large computational load.


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Wavelet decomposition of an image

y In this paper the well known Daubechies wavelet Db4 is

adopted .

y An image is decomposed into four subbands.

y The band LL is a coarser approximation to the original

image.

y The bands LH and HL record respectively the changes of

the image along horizontal and vertical directions while

the HH band shows the higher frequency component ofthe image.


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Wavelet decomposition of an image

y The decomposition can be further carried out for the

LL subband.

y After applying a three-level Wavelet transform, an

image is decomposed into subbands of different

frequency.

y if the resolution of an image is 128x128 the

subbands1,2,3,4 are of size 16x16, the sub bands

5,6,7 are of size 32x32 and the subbands 8,9,10 are

of size 64x64.


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MLP Neural Network

y Input for hidden layer is

given by

y The units of output vector of

hidden layer after passing

through the activation

function are given by


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MLP Neural Network

In same manner, input for

output layer is given by

and the units of output vector of

output layer are given by


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MLP Neural Network

For updating the weights, we

need to calculate the error. This

can be done by

y After calculating the weight

change in all layers, the weights

can simply updated by

y This process is repeated, until

the error reaches a minimumvalue


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Proposed Method


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Proposed Method


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Face database

y In this paper, experiments are based on ORL face

database.

y ORL face database contains 40 distinct persons, each

person having ten different face images.y There are 400 face images in total, with 256 gray

degrees and the resolution of 112*92.

y These face images are attained in different situations,

such as different time, different angles,and different face

details.


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Experimental Result


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Experimental Result


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Experimental Result


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Conclusion

y The experiments that we have conducted on the ORL

database vindicated that the combination of Wavelet,

PCA and MLP exhibits the most favorable performance,

on account of the fact that it has the lowest overalltraining time, the lowest redundant data, and the highest

recognition rates when compared to similar so-far

introduced methods.

y Our proposed method in comparison with the present

hybrid methods enjoys from a low computation load in

both training and recognizing stages. As another

illustration of the privileges of our introduced method,

we can mention its great precision.


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Matlab Implemention

Documents

Wavelet Presentation