Face Recognition Algorithm Optimization

7/30/2019 Face Recognition Algorithm Optimization

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Limitations of existing algorithms. Objective.

Project selection.Algorithm and its flow.

Implementation.

Applications.

Future Enhancement.


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The existing face recognition algorithms use thewhole face to recognize wherein number ofcomputations are more .

Power consumption .

Computation time.


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Implementation.

Applications.

Future Enhancement.


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According to the statistics, the faces of85-90% of the people exhibit reflectionalsymmetry about a bilateral symmetry axis.

The use of the average half face in face

recognition research has shown a potentialincrease in accuracy and decrease in storageand computation time .


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The project aims to authenticate the face byapplying algorithm only for half face

thereby reducing the number ofcomputations and power

consumption.


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Implementation.

Applications.

Future Enhancement.


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Algorithm: MATLAB implementation of face recognitionprofile matching

Database: MATLAB development of file systemData Acquisition: Multimedia Lab video camera or digitalcamera


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Implementation.

Applications.

Future Enhancement.


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I. Centre and orient the face in the image.

II. Partition the image into two equal left andright halves, lh and rh.

III. Reverse of the ordering of(or mirror) thecolumns of one of the images producing rh1.

IV.Average the resulting mirror right halfimage(rh1) with the left half image(lh) to

produce the average-half-face.V. The average half face is then in used in place

of the full face image for face recognition.


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Eigen faces are used because of its simplicity and ithighlights the difference in face recognition accuracybetween using the full face and the average half face.

Eigen faces is based on the principal components analysis(PCA) .

PCA captures as much of the variance in the data as possiblein as few principal components as possible


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START

FACE DETECTION

COMPUTATION OFAVERAGE-HALF-FACE

IMAGE ACQUISITION

COMPARISION WITHDATABASE

DECISION MAKING

END


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Implementation.

Applications.

Future Enhancement.


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The 2D image for which the computation is to be done isopened using MATLAB application.

Facial part of the subject is extracted.

Average half face of image is obtained.

Using PCA algorithm the average half face is comparedagainst the 2D images present in the database.

Later decision is made.


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Implementation.

Applications.

Future Enhancement.


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(We will collect it frominternet)


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Implementation.

Applications.

Future Enhancement.


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We will use symmetry scores to compare mostsymmetric and least symmetric sub-groups.

Future work will include introducing additional

symmetry measures as well as extending this analysisto more databases of 2D and 3D faces.

The ultimate goal would be to create a correlationbetween the symmetry of the face and face

recognition that could be used to improve the overallface recognition accuracy.


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Implementation.

Applications.

Future Enhancement.

Face Recognition Algorithm Optimization

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