face recognition under uncontrolled indoor environment

8/3/2019 Face Recognition Under Uncontrolled Indoor Environment

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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

8

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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

face recognition under uncontrolled indoor environment

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