Terrorists Team members: Ágnes Bartha György Kovács Imre Hajagos Wojciech Zyla.
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Transcript of Terrorists Team members: Ágnes Bartha György Kovács Imre Hajagos Wojciech Zyla.
![Page 1: Terrorists Team members: Ágnes Bartha György Kovács Imre Hajagos Wojciech Zyla.](https://reader036.fdocuments.net/reader036/viewer/2022062720/56649f045503460f94c184f9/html5/thumbnails/1.jpg)
Terrorists
Team members:Ágnes Bartha
György Kovács
Imre Hajagos
Wojciech Zyla
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The Project
• What is our goal?• Who are they?• How to start?• How ro recognize face?
– Face detection– Face feature detection– Eyes, mouth, nose
related search
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What is our goal?
• Find out if someone is a terrorist.
• Try to identify then even if they
are disguised• We have a problem..
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Who are they? • They are who
– Blow up cars, buildings
– Kill people
– Undertake control
• Enough reason to do something
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How to start?• Database
– Images of terrorist
– Training images for identification ( by computer)
• Take a picture of suspicious person
• Start to do a program that decides if someone is a terrorist
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How to recognize face?
• Problems – Disguised person– Other : rotated head, glasses.
• Use some algorithms– PCA– LDA
• OpenCV– Haar object detection– AdaBoot
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PCA• Principal Component Analysis
• reduce the dimensionality of the data while retaining as much as possible of the variation present in the original dataset
• implies information loss
• The best low-dimensional space can be determined by the "best„ eigenvectors of the covariance matrix
• (i.e., the eigenvectors corresponding to the "largest" eigenvalues, also called "principal components").
• PCA projects the data along the directions where the data varies the most.
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Problems of Eigenface technique
• Sensitive to rotation, scale and translation.• Sensitive to lighting variations• Background interference
• Face images should be preprocessed to lessen the effects of possible variations.
• Variations such as lighting and rotation can also be taken into account during training. The training dataset may include samples with such variations.
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LDA• Linear Discriminant Analysis• The objective of LDA is to perform
dimensionality reduction while preserving as much of the class discriminatory information as possible
• It seeks to find directions along which the classes are best separated.
• It does so by taking into consideration the scatter within-classes but also the scatter between-classes.
• It is also more capable of distinguishing image variation due to person identity from variation due to other sources such as illumination and expression.
• μr mean feature vector for class r.• Kr number of training samples from class r.• LDA computes a transformation that
maximizes the between-class scatter while minimizing the within-class scatter:
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LDA 2.
• Limitations:– at most R−1 nonzero eigenvalues.– matrix Sw
-1 does not always exist.• need at least N +R training samples – not practical
• Use PCA to reduce dimention• When the number of training samples is large and representative for each class,
LDA outperforms PCA.
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OpenCV
• Open Source Computer Vision Library– Extensive vision suport
• Convolution, thresholding, floodfills, histogramming• Pyramidal-subsampling• Learning-based vision• Feature detection
– Edge detection
– Blob finders ,. ....
– Haar cascade classifier
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IplImage
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OpenCV -- Haar• OpenCV has a Haar features based face
detection module.
• Uses local features such as edges and line patterns. It scans a given image at different scales as in template matching.
• Scale, translation and light invariant.
• However it is sensitive to rotation.– Rotate image and run again
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Advantages of using OpenCV Haar object detection
• Face detector already implemented
• Its only argument is a xml file
• Detection at any scale
• Face detection (for videos) at 15 frames per second for 384*288 pixel images
• 90% objects detected – achievable doing 2 weeks training
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Haar-Like Features
• Each Haar-like feature consists of two or three jointed “black” and “white” rectangles:
• The value of a Haar-like feature is the difference between the sum of the pixel gray level values within the black and white rectangular regions:
f(x)=Sumblack rectangle (pixel gray level) – Sumwhite rectangle (pixel gray level) • Compared with raw pixel values, Haar-like features can reduce/increase
the in-class/out-of-class variability, and thus making classification easier.
Figure 1: A set of basic Haar-like features.
Figure 2: A set of extended Haar-like features.
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Haar-Like Features
• The rectangle Haar-like features can be computed rapidly using “integral image”.
• Integral image at location of x, y contains the sum of the pixel values above and left of x, y, inclusive:
• Haar features computed in constant time
• The sum of pixel values within “D”:
yyxx
yxiyxP','
)','(),(
A B
C D
P2
P3 P4
P1
P (x, y)
DCABADCBAAPPPP
DCBAPCAPBAPAP
3241
4321 ,,,
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Adaboost classifier
• Selects a small number of critical visual features
• Combines a collection of weak classification functions to form a strong classifier
The first and second features selected by AdaBoost for face detection
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2. Haar-Like Features (cont’d)
• For example: to detect hand, the image is scanned by a sub-window containing a Haar-like feature.
• Based on each Haar-like feature fj , a weak classifier hj(x) is defined as:
where x is a sub-window, and θ is a threshold. pj indicating the direction of the inequality sign.
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Adaboost• The computation cost using Haar-like features:
Example: original image size: 320X240, sub-window size: 24X24,The total number of sub-windows with one Haar-like feature per second: (320-24)X(240-24)=63,936
Considering the scaling factor and the total number of Haar-like features, the computation cost is huge.
• AdaBoost (Adaptive Boost) is an iterative learning algorithm to construct a “strong” classifier using only a training set and a “weak” learning algorithm. A “weak” classifier with the minimum classification error is selected by the learning algorithm at each iteration.
• AdaBoost is adaptive in the sense that later classifiers are tuned up in favor of those sub-windows misclassified by previous classifiers.
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Adaboost
• The algorithm:
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Adaboost starts with a uniform distribution of “weights” over training examples. The weights tell the learning algorithm the importance of the example.
Obtain a weak classifier from the weak learning algorithm, hj(x).
Increase the weights on the training examples that were misclassified.
(Repeat) At the end, carefully make a linear combination of the weak classifiers obtained at all iterations.
)()()( ,11,final xxx nnfinalfinal hhf
Adaboost
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Adaboost
• Simple to implement
• But.. – Suboptimal solution– Over fit in presence of noise
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The Cascade of Classifiers
• A series of classifiers are applied to every sub-window.
• Increases speed
• The first classifier eliminates a large number of negative sub-windows and pass almost all positive sub-windows (high false positive rate) with very little processing.
• Subsequent layers eliminate additional negatives sub-windows (passed by the first classifier) but require more computation.
• After several stages of processing the number of negative sub-windows have been reduced radically.
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The Cascade of Classifiers
• Negative samples: non-object images. Negative samples are taken from arbitrary images. These images must not contain object representations.
• Positive samples: images contain object (hand in our case). The hand in the positive samples must be marked out for classifier training.
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image
detecting
face
detecting
features
creating
cropping
face
normalizing
featurevector
001. . .
010. . .
Database of terrorists
comparing vectors
results
is not in thedatabase
144x150 90x130
256x256
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Eye detection with Haar
• eye_haarcascade_classifier• create a growable
sequence of eyes• detect the objects• store them in
the sequence
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Thank you for your attention