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DEPARTMENT OF INFORMATION SCIENCE & ENGINEERING
FACE RECOGNITIONUSING SIFT FEATURES
Presented by,
Nouman Sadiq(1PI08IS055)
Deep Agarwal (1PI08IS034)
Department of ISE
PESIT,
Bangalore
Internal guide,
Prof. Shylaja S SH.O.D
Department of ISE
PESIT,
Bangalore
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AGENDA
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INTRODUCTION1
PROJECT BRIEF2
IMPLEMENTATION3
4 CONCLUSSION
5 FURTHER ENHANCEMENT
6 BIBLIOGRAPHY
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PROJECTOVERVIEW
Scope of this project?
Distinctive invariant features from a
face is obtained. It is applied to the training set of the
faces, thus transforming it.
The face in the test set is matched.3
Goal of this project?
To extract Scale Invariant Featuresfrom a face that can be further used
to perform reliable face recognition.
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Face recognition can be categorized into three steps
Face detection
Feature extraction
Face recognition
Face recognition is always prone to problems like
Change of posture
Illumination changes
Change of environment
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Face recognition??
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Stands for Scale Invariant Feature
Tranformation
David G. Lowe Introduced this algorithm.
Here image features having properties which
makes them suitable for matching differingimages of the same face are extracted.
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Introduction
- S.I.F.T
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SIFT ALGORITHM OVERVIEWFollowing are the major stages of computation used to
generate the set of image features:
1. Scale-space extrema detection
2. Keypoint localization
3. Orientation Assignment
4. Keypoint Descriptor construction
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It involves the following steps
1.1 : The face is expressed as octaves ofdifferent size. I(x,y)
1.2 : Each images of an octave issmoothed with different scales ofthe Gaussian function. L(x,y)
1.3 : Compute difference of Gaussian(doG) images from adjacent scalesfor entire octave D(x,y).
1.4 :From difference-of-Gaussian localextrema detection we obtainapproximate values for keypoints (orinteresting points)
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The input image
The input image
with interesting
points
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STEP1:SCALE-SPACEEXTREMADETECTION
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STEP1:SCALE-SPACEEXTREMADETECTION
1.1 : The face is expressed as octaves of different size
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Four Different Octaves
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1.2 : All the octave images are filtering with Gaussianfunction thus obtaining different scales.
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Where
The scale space of image L(x, y, ), that is produced from
the convolution of a variable-scale Gaussian, G(x, y, )withan input image, I(x, y):
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STEP1:SCALE-SPACEEXTREMADETECTION
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1.2 : All the octave images are filtering with Gaussianfunction thus obtaining different scales.
10The second Octave of L images
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Five Different scales of
Gaussian blur()
STEP1:SCALE-SPACEEXTREMADETECTION
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1.3 : The difference of the different scale images isfound.
11The second Octave of D images
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STEP1:SCALE-SPACEEXTREMADETECTION
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1.3 : The difference of the different scale images isfound.
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STEP1:SCALE-SPACEEXTREMADETECTION
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STEP1:SCALE-SPACEEXTREMADETECTION
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1.4 :From difference-of-Gaussian local extrema detectionwe obtain approximate values for keypoints
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Maxima and minima of thedifference-of-Gaussianimages are detected bycomparing apixel (marked with X) to its26 neighbours in 3x3
regions at the current andadjacent scales (markedwith circles)
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STEP1:SCALE-SPACEEXTREMADETECTION
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1.4 :From difference-of-Gaussian local extrema detection weobtain approximate values for keypoints
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STEP1:SCALE-SPACEEXTREMADETECTION
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STEP 2:KEYPOINTLOCALIZATION
It involves the following steps
2.1 : The interesting points of very Low
contrast are removed
2.2 : Some more points are removedwhich threshold on ratio of principalcurvatures.
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The image with the
interesting points
The image with the
keypoints
Here the keypoints are selected basedon measures of their stability.
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STEP 2:KEYPOINTLOCALIZATION
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2.1 : The interesting points of very Lowcontrast are removed
The image with the
interesting points
The image with the
keypoints
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STEP 2:KEYPOINTLOCALIZATION
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2.2 : Some more points are removed which thresholdon ratio of principal curvatures.
Difference-of-Gaussian function will be strong
along edgesSome locations along edges will have a large
principal curvature across the edge but a smallprincipal of curvature perpendicular to the edge
Therefore we need to compute the principalcurvatures at the location and compare the two.
Then eliminate some of the candidates belowthreshold
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STEP 2:KEYPOINTLOCALIZATION
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The image with
interesting points
The image with the
keypoints
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STEP 2:KEYPOINTLOCALIZATION
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436 interesting
points
The points below
contrast threshold is
eliminated.410
points eliminated
26 interesting
points are left
Further points
whose curvature
ratio is above the
threshold is also
removed. 13 more
points are removed.
The final face with
the 13 keypoints.
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STEP 3:ORIENTATIONASSIGNMENT
For image sample, L(x, y), the gradient magnitude,
m(x, y), and orientation, teta(x, y), is computed usingpixel differences:
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Points in region around keypoint are selected andmagnitude and orientations of gradient arecalculated.
22))1,()1,(()),1(),1((),( yxLyxLyxLyxLyxm
))),1(),1(/())1,()1,(((tan),(1
yxLyxLyxLyxLyx
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TRAININGFACESET
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TESTFACESET
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EXTRACTING KEYPOINTS
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Video 1
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EXTRACTING KEYPOINTS
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Video 2
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Two images of the faces of a different persons.
0 keypoints are matched
MATCHING
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CONCLUSION
In this project Scale Invariant Feature Transform (SIFT) is
implemented for feature extraction for the purpose of face
recognition.
The Comparisons of this approach among other holistic
approaches and feature based approaches are yet to be
done .
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Further Enhancement
There are many directions for further research in deriving
invariant and distinctive face Features, further distinctiveness
could be derived from including illumination-invariant color
descriptors .
Another direction for future research will be to individually
learn features that are suited to recognizing particular
categories of facial images.
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