dc face recognition

8/6/2019 DC Face Recognition

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FACE RECOGNITION, EXPERIMENTS

WITH RANDOM PROJECTION

Navin Goel

Graduate Student

Advisor: Dr George Bebis

Associate Professor

Department Of Computer Science and Engineering

University of Nevada, Reno


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Overview

Introduction and Thesis Scope

Principal Component Analysis

Method of Eigenfaces

Random Projection

Properties of Random Projection

Random Projection for Face Recognition

Experimental Procedure and Data sets

Recognition approaches and results

Conclusion and Future work


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Introduction

Problem Statement Identify a persons face image from

face database.

Applications Human-Computer interface,

Static matching of photographs,

Video surveillance,

Biometric security,

Image and film processing.


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Challenges

Variations in pose Head positions, frontal view, profile

view and head tilt, facial expressions

Illumination Changes Light direction and intensity changes,

cluttered background, low quality

images

Camera Parameters Resolution, color balance etc.

Occlusion Glasses, facial hair and makeup


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Thesis Scope

Investigate the application of Random Projection (RP) in Face

Recognition.

Evaluate the performance of RP for face recognition under various

conditions and assumptions.

Aim at proposing an algorithm, which replaces the learning step ofPCA by cheaper and efficient step.


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Principal Component Analysis (PCA)

For a setMofN-dimensionalvectors {x1, x2xM}, PCA finds the

eigenvalues and eigenvectors of the covariance matrix of the vectors

? A? AT

M

i

ii xxM

C !

!1

1 QQ - the average of theimage vectors

an image as1d vector kkk

uu P!7 uk - Eigenvectors

k- Eigenvalues

Keep only keigenvectors, corresponding to the k largest eigenvalues.


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Method of Eigenfaces

Apply PCA on the training dataset

Project the Gallery set images to the reduced dimensional

eigenspace.

For each test set image:

Project the image to the reduced dimensional

eigenspace.

Measure similarity by calculating the distance between

the projection coefficients of two datasets

The face is recognized if the closest gallery image

belongs to same person in test set


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Random Projection (RP)

The originalN-dimensionaldata is projected to a d-dimensionalsubspace, (d


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Random Projection Data Independence

Two 1-separated

spherical Gaussians

were projected onto a

random space ofdimension 20.

Error bars are for1

standard deviation and

there are 40

trials perdimension.

Digital images,

document databases,

signal processing.

Random Projection does not depend on the data itself.

S. Dasgupta. Experiments with Random Projection. Uncertainty in Artificial Intelligence, 2000.


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Random Projection Eccentricity

Gaussian in subspace

of 50

-dimension andeccentricity 1,000 is

projected onto lower

dimensions.

Conceptually easier todesign algorithms for

spherical clusters than

ellipsoidal ones.

RP makes highly eccentric Gaussian clusters to spherical.



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Random Projection Complexity

Number of floating-point

operations needed when

reducing the

dimensionality of image

data using RP (+), SRP(*), PCA () and DCT

(), in a logarithmic

scale.

Complexity of RP is of the order of quadratic (n2) in contrast to

PCA which is cubic (n3).

E. Bingham and H. Mannila. Random projection in dimensionality reduction: applications to image

and text data.Proceedings of the 7th ACM SIGKDD International Conference on Knowledge

Discovery and Data Mining, pp. 245-250, August 26-29, 2001.


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Random Projection Lower Bound

1-separated mixtures ofk

Gaussians of dimension100 was projected on d =

lnk.

PCA cannot be expected

to reduce thedimensionality ofk

Gaussians below (k).

What value ofd(lower space) must be chosen ?



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Random Projection for Face Recognition

Generate lower dimensional random subspace.

Project the Gallery set images to the reduced dimensional

random space.

For each test set image:

Project the image to the reduced dimensional

random space.

Measure similarity by calculating the distance

between the projection coefficients of two datasets.

The face is recognized if the closest gallery image

belongs to same person in test set.


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Experimental Procedure

Main steps of the approach


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Data Sets

Face images from ORL

data set for a particular

subject.

Face images from CVL


subject.

Face images from AR


subject.


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Closest Match Approach

Averaging over 5

experiments.

Flowchart for

calculating recognitionrate using closest match

approach.


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Closest Match Approach + Majority Voting

Flowchart for

calculating recognition

rate using closest match

approach + majority

voting technique.


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Closest Match Approach + Scoring

Flowchart for

calculating recognition

rate using closest match

approach + scoring

technique.


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Results for the ORL database

Experiment on ORL database using closest match approach + majority voting technique,

where training set consists of same subjects as in the gallery and testing set.

Experiment on ORL database using closest match approach + majority voting

technique, where training set consists of different subjects as in the gallery andtestin set.


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Results for the CVL database

Experiment on CVL database using closest match approach + majority voting technique,

where training set consists of same subjects as in the gallery and testing set.

Experiment on CVL database using closest match approach + majority voting,

training set consists of different subjects as in the gallery and testing set.


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Results for the AR database

Experiment on AR database using closest match approach + majority voting, training set

consists of random subjects, gallery and Test set contains different combinations.


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ORL database for Multiple Ensembles

Plot on RCA, Majority-Voting technique for 5 and 30 different random seeds, training set

consists of different subjects as in the gallery and testing set.


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Results for the ORL database with Scoring Technique

Experiment on ORL database using closest match approach + scoring, training set consists

of different subjects as in the gallery and testing set.

Experiment on ORL database using closest match approach + scoring, training set consists

of same subjects as in the gallery and testing set.


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Results for the CVL database with Scoring Technique

Experiment on CVL database using closest match approach + scoring, training set consists

of different subjects as in the gallery and testing set.


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Results for the AR database with Scoring Technique

Experiment on AR database using closest match approach + scoring, training set consists of

random subjects as in the gallery and Test set contains different combinations.


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Conclusion

We were able to get recognition rate equivalent to PCA and in most cases

better than it.

RP matrix is independent of the training data.

The main advantage of using RP is the computational complexity, for RP

it is quadratic and for PCA cubic.

RP works better when gallery to test set ratio is higher.

RP works better than PCA when the training set images differ from

gallery and test set.

RP shows irregularity for single runs, but improves with multiple

ensembles. Majority-voting over closest match for recognition further improves the

performance of RP.

For scoring technique, greater the number of top hits per image, better the

performance.


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Future Work

Combine different random ensembles, that will improve

efficiency and accuracy.

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