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Kamal Adel
Supervised by[Dr.Khaled Assaleh][Dr.Tamer Shanableh]
Face recognition underuncontrolled indoor
environment
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In this presentation
Motivation and objectives.
Biometrics.
Face recognition.
Pattern recognition.
SCface database.
Methodology.
Preliminary results.
Future work.
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Motivation
There is an increasing demand for security.
Many other potential uses for face
recognition are now being developed. LikeATM machines, online access, andelections.
This study uses a recently publisheddatabase called SCface database.
The researchers who published SCface
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Objectives
Examine the effect of camera quality anddistance from camera on several facerecognition techniques.
Study the consequences of imageenhancement on face recognition.
Develop a reliable algorithm thatrecognizes faces captured by differentcameras in terms of quality and resolution
at different distances
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Biometric technologies
Identifying people based on theirphysiological characteristics or behavioralqualities.
physiological behavioralFacial recognition Signature recognition
Fingerprint recognitionVoice recognition
Hand geometry Gait recognition
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Selecting an identifier
Universality.
Distinctiveness.
Permanent
Collectability
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Face recognition system
Automatically Identifying or verifyingperson from a digital image or a videoframe.
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Modes of operation
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Face recognition
Face recognition may not be the mostreliable and efficient among all biometrics.
However, one key advantage is that it doesnot require aid from the test subject.
Pattern recognition algorithm is needed.
Some algorithms follow appearance-basedmethod and some other algorithms followmodel-based approaches.
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Pattern recognition
Pattern recognition is a subfield of ArtificialIntelligence.
Recognizing a correspondence betweenfeatures that represent samples or datapoints
Four stages:
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Linear classifier
Classification based on a linearcombination of the features
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K Nearest Neighbors
selecting the k nearest neighbors to theunknown point and uses a majority vote todetermine the class of that point
10 NN
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Neural networks
A neural network consists of input layer,one hidden layer at least and output layer
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Modes of operation
Feed forward mode: inputs are fed throughthe network to obtain outputs.
Outputs are then subtracted from a desiredoutput to measure the error. This will startlearning mode.
Weights of the neural network are to beupdated base on the error obtained.
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SCface database
static images of 130 different people.
Images were taken by five different
surveillance cameras at three previouslymarked positions.
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Images at distance 1 & 2
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Images at distance 3
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IR night vision mode
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Different angles
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SCface versus otherdatabases
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Methodology
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Preliminary results
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Different mask sizes
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Curse of dimensionality
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One training camera
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Night mode effect
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Using LPF
0
2
4
6
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Segmentation
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Segmentation
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KNN classifier
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Eigen faces
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Eigen faces
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Future work
Investigate and boost the performance ofthe Eigenface classifier, consideringvariable number of Eigenface.
Apply polynomial expansion and spectralregression and examine their effects on therecognition rate.
Use the neural network classifier and
analyze its performance.Investigate new features
Utilize both mug shots and different posesto improve the recognition rate