2013

BIOMETRICS

Facial Recognition System Developing an algorithm for facial recognition

system using Image processing in Matlab

T. Lavanya,EEE,NITK

Under the guidance of Professor DR. K.B.Raja, Department of Electronics and

Communication, UVCE, Bangalore.

1

1.INTRODUCTION

Biometrics:

Biometrics or biometric authentication refers to the identification of

humans by their characteristics or traits (distinguishing features). It is

used as a form of identification, and access control, also to identify

individuals in groups that are under surveillance. Biometric identifiers

are the distinctive, measurable characteristics used to label and describe

individuals. They are often categorized as physiological versus

behavioral characteristics.

1. Physiological characteristics are related to the shape of the body.

Examples include fingerprint, face recognition, DNA, Palm print,

hand geometry, iris recognition, retina and odour /scent.

2. Behavioral characteristics are related to the behavior of a person,

including typing rhythm, gait, and voice.

Behaviometrics has been coined by researchers to describe the latter

class of biometrics.

What is the need for Biometrics?

Access control through token based identification systems, such as a

driver's license or passport, and knowledge based identification systems,

such as a password or personal identification number have become

traditional ways and turned to be inefficient. Since biometric identifiers

are unique to individuals, they are more reliable in verifying identity

than token and knowledge based methods leads to opt biometrics

2

How does Biometrics function?

Different aspects of human physiology, chemistry or behavior can be

used for biometric authentication. The selection of a particular biometric

for use in a specific application involves a weighting of several factors.

Jain et al. (1999) has identified seven such factors to be used when

assessing the suitability of any trait for use in biometric authentication.

1. Universality means that every person using a system should

possess the trait.

2. Uniqueness means the trait should be sufficiently different for

individuals in the relevant population such that they can be

distinguished from one another.

3. Permanence relates to the manner in which a trait varies over

time. More specifically, a trait with 'good' permanence will be

reasonably invariant over time with respect to the specific

matching algorithm.

4. Measurability (collectability) relates to the ease of acquisition or

measurement of the trait. In addition, acquired data should be in a

form that permits subsequent processing and extraction of the

relevant feature sets.

5. Performance relates to the accuracy, speed, and robustness of

technology used.

6. Acceptability relates to how well individuals in the relevant

population accept the technology such that they are willing to have

their biometric trait captured and assessed.

7. Circumvention relates to the ease with which a trait might be

imitated using an artifact or substitute.

No single biometric will meet all the requirements of every

possible application.

3

Basic block diagram of a biometric system in 2 modes

In verification mode the system performs a one-to-one comparison of a

captured biometric with a specific template stored in a biometric

database in order to verify the individual is the person they claim to be.

Three steps involved in person verification.

In the first step, reference models for all the users are generated and

stored in the model database.

In the second step, some samples are matched with reference models to

generate the genuine and impostor scores and calculate the threshold.

Third step is the testing step. This process may use a smart card,

username or ID number (e.g. PIN) to indicate which template should be

used for comparison. 'Positive recognition' is a common use of

verification mode, "where the aim is to prevent multiple people from

using same identity".

4

The first time an individual uses a biometric system is called enrollment.

During the enrollment, biometric information from an individual is

captured and stored. In subsequent uses, biometric information is

detected and compared with the information stored at the time of

enrollment. Note that it is crucial that storage and retrieval of such

systems themselves be secure if the biometric system is to be robust.

The first block (sensor) is the interface between the real world and the

system; it has to acquire all the necessary data.

The second block performs all the necessary pre-processing: it has to

remove artifacts from the sensor, to enhance the input (e.g. removing

background noise), to use some kind of normalization, etc.

In the third block necessary features are extracted. This step is an

important step as the correct features need to be extracted in the optimal

way. A vector of numbers or an image with particular properties is used

to create a template. A template is a synthesis of the relevant

characteristics extracted from the source.

Elements of the biometric measurement that are not used in the

comparison algorithm are discarded in the template to reduce the file

size and to protect the identity of the enrollee.

If enrollment is being performed, the template is simply stored

somewhere (within a database). If a matching phase is being performed,

the obtained template is passed to a matcher that compares it with other

existing templates, estimating the distance between them using any

algorithm (e.g. Hamming distance). The matching program will analyze

the template with the input. This will then be output for any specified

use or purpose (e.g. entrance in a restricted area)

5

Performance:

The following are used as performance metrics for biometric systems:

False Accept Rate Or False Match Rate (FAR or FMR): The

probability that the system incorrectly matches the input pattern to a

non-matching template in the database. It measures the percent of

invalid inputs which are incorrectly accepted. In case of similarity scale,

if the person is imposter in real, but the matching score is higher than the

threshold then he is treated as genuine and that increases the FAR and

hence performance also depends upon the selection of threshold value.

False Reject Rate Or False Non-Match Rate (FRR Or FNMR): The

probability that the system fails to detect a match between the input

pattern and a matching template in the database. It measures the percent

of valid inputs which are incorrectly rejected.

Receiver Operating Characteristic Or Relative Operating

Characteristic (ROC): The ROC plot is a visual characterization of the

trade-off between the FAR and the FRR. In general, the matching

algorithm performs a decision based on a threshold which determines

how close to a template the input needs to be for it to be considered a

match. If the threshold is reduced, there will be fewer false non-matches

but more false accepts. Correspondingly, a higher threshold will reduce

the FAR but increase the FRR. A common variation is the Detection

error trade-off (DET), which is obtained using normal deviate scales on

both axes. This more linear graph illuminates the differences for higher

performances (rarer errors).

Equal Error Rate Or Crossover Error Rate (EER Or CER): The rate

at which both accept and reject errors are equal. The value of the EER

can be easily obtained from the ROC curve. The EER is a quick way to

6

compare the accuracy of devices with different ROC curves. In general,

the device with the lowest EER is most accurate.

Failure To Enroll Rate (FTE Or FER): The rate at which attempts to

create a template from an input is unsuccessful. This is most commonly

caused by low quality inputs.

Failure To Capture Rate (FTC): Within automatic systems, the

probability that the system fails to detect a biometric input when

presented correctly.

Template Capacity: The maximum number of sets of data which can

be stored in the system.

Selection of biometrics in any practical application depending upon the

characteristic measurements and user requirements We should consider

Performance, Acceptability, Circumvention, Robustness, Population

coverage, Size, Identity theft deterrence in selecting a particular

biometric. Selection of biometric based on user requirement considers

Sensor availability, Device availability, Computational time and

reliability, Cost, Sensor area and power consumption.

As a proverb says, “face is the index of mind”, face is the first and

foremost feature in a human being which distinguishes one from

another. A person is identified or remembered by his face which is

unique for every person (except in some cases). Using biometrics of

facial recognition is preferred since many systems which require

access control has the identity as the image of face of the person rather

than signature, palm or finger print and if surveillance cameras are

being used it is easy to capture ones face without the person knowing

it, which is not possible with signature, palm or finger print.

7

2. FACIAL RECOGNITION SYSTEM

A facial recognition system is a computer application for

automatically identifying or verifying a person from a digital image

or a video frame from a video source. One of the ways to do this is by

comparing selected facial features from the image and a facial

database. It is typically used in security systems.

Facial recognition algorithms identify facial features by extracting

landmarks, or features, from an image of the subject's face. For

example, an algorithm may analyze the relative position, size, and/or

shape of the eyes, nose, cheekbones, and jaw. These features are then

used to search for other images with matching features. Other

algorithms normalize a gallery of face images and then compress the

face data, only saving the data in the image that is useful for face

detection. A probe image is then compared with the face data. One of

the earliest successful systems is based on template matching

techniques applied to a set of salient facial features, providing a sort

of compressed face representation. Recognition algorithms can be

divided into two main approaches, geometric, which look at

distinguishing features, or photometric, which is a statistical approach

that distills an image into values and compares the values with

templates to eliminate variances.

In the present world face recognition finds it use in every walk of life.

Starting with smart phones, digital cameras, tabs, laptops and

televisions, all are coming with this feature of face recognition

enabling their use in more personalized way. Almost every place

either a public place like banks, airports, government offices,

universities or private (business) places like shopping malls are being

watched by surveillance cameras which can be used for tracking a

suspicious person or in general anyone using face recognition.

8

Google's Picasa digital image organizer has a built in face recognition

system.

Sony's Picture Motion Browser (PMB) analyses photo, associates

photos with identical faces so that they can be tagged accordingly,

and differentiates between photos with one person, many persons and

nobody.

Windows Live Photo Gallery includes face recognition

Apple's iPhoto image organizer has a feature named Faces which

allows users to associate names to faces in a series of photographs.

The software then proposes other matching faces it locates in the

photo library and gradually refines its recognition, according to the

user's acceptance, denial or renaming of the proposed face.

Even social networking sites like facebook use tagging photos of a

person based on this facial recognition

This technology could be used as a security measure at ATM’s; where

the ATM would capture an image of your face, and compare it to your

photo in the bank database to confirm your identity.

This same concept is being used in computers by using a webcam to

capture a digital image of your face which replaces the necessity of

password as a means to log-in.

Another use could be a portable device to assist people with

prosopagnosia in recognizing their acquaintances.

COMPARITIVE STUDY: Though facial recognition is used widely,

among the different biometric techniques, facial recognition may not be

the most reliable and efficient. However, one key advantage is that it

does not require aid (or consent) from the test subject. Properly designed

systems installed in airports, multiplexes, and other public places can

identify individuals among the crowd. Other biometrics like fingerprints,

iris scans, and speech recognition cannot perform this kind of mass

identification. However, questions have been raised on the effectiveness

of facial recognition software in cases of railway and airport security.

9

Two algorithms are being used in this project, first is using preprocessed

image, i.e. comparison of images in spatial domain by considering the

pixel values.

Algorithm 1: Spatial domain

In general the face recognition is carried out in four phases

a) Image Acquisition

b) Preprocessing

c) Feature Extraction and

d) Matching

Database Face images Text face images

Resize (128*128)

Co-efficient vector

Euclidian Distance classifier

Face image match/non match

a) Image Acquisition: Images are acquired from open source

databases like ORL, JAFFE, Indian male, Indian female and more.

Images in each database consist of various images of each person

taken at different modes such as variation in pose, illumination,

facial expressions and with different accessories

10

i) At variety of pose angles to permit testing of pose invariance

ii) With a wide variety of illumination angles to permit testing of

illumination invariance and

iii) Under a variety of commonly encountered illumination color

temperatures to permit testing of illumination color invariance.

b) Preprocessing: Preprocessing is done to extract the features of the image with minimal

difficulties such as resizing the image. Resizing makes all the images in

the database into a particular size which is common to all the images.

c) Feature Extraction:

From the preprocessed images features are extracted, in this project, the

pixel values of each image are stored in a vector and all the images to be

considered in the database are stored in the form of vector of size equal

to the total number of images.

If a database is considered with n persons and m images of each person,

a database should be created which contains x (say x<n) persons and y

(say y<m) images of each person and the remaining persons and images

are considered for test database.

The first three steps are common for both database and test database.

d) Matching: The features of database and test database are compared by using a

classifier which in this case is Euclidean distance. Each test image is

compared with every image present in the database. The Euclidean

distance is zero if same image is considered. If test image belongs to a

person whose images are already there in database, then the resulting

Euclidean distance should be minimum or else it should be maximum.

Implementation using Matlab: Based on the above algorithm, a matlab code has been developed to

calculate, plot the performance parameters namely FAR,FRR,TSR and

obtain EER from the results. JAFFE Database is being considered.

Results for one test case are shown below:

No of persons in database=7; No of persons in test database=10;

No of images of each person in database=12; Test image=17;

Image size = 128*128;

11

1. FRR versus Threshold

2. FAR versus Threshold

12

3. TSR versus Threshold

4. FAR, FRR and TSR versus threshold

13

The EER is 0.142857 corresponding to the optimum threshold of

0.449611

The TSR is 0.857143 corresponding to the optimum threshold of

0.449611

Database: JAFFE

The following table gives the details of results when the same algorithm

and Matlab code applied on various test images by changing number of

persons in database, number of images of each person and test image.

No. of

persons

in

database

No. of

persons

out of

database

Number

of

images

of each

person

Number

of the

test

image

EER TSR Optimum

threshold

preprocessed

5 5 15 17 0.100000 0.900000 0.432737

5 5 15 18 0.000000 1.000000 0.411111

5 5 15 19 0.200000 0.800000 0.455770

5 5 15 20 0.200000 0.800000 0.458616

7 3 15 17 0.000000 1.000000 0.444960

7 3 15 18 0.000000 1.000000 0.459256

7 3 15 19 0.142857 0.857143 0.462261

7 3 15 20 0.142857 0.857143 0.482867

6 4 15 17 0.000000 1.000000 0.452415

6 4 15 18 0.000000 1.000000 0.441222

6 4 15 19 0.181818 0.818182 0.455621

6 4 15 20 0.166667 0.833333 0.464738

7 3 12 13 0.000000 1.000000 0.445503

7 3 12 15 0.000000 1.000000 0.477148

7 3 12 17 0.142857 0.857143 0.452712

It has been observed the algorithm gives EER values in the range of 0.20

to 0.00 and TSR values in range of 0.80 to 1.00 for this database.

14

Algorithm 2: Using DWT on preprocessed image (Discrete wavelet

transform)

Discrete Wavelet Transform:

The transform of a signal is just another form of representing the signal.

It does not change the information content present in the signal. The

Wavelet Transform provides a time-frequency representation of the

signal. It was developed to overcome the short coming of the Short Time

Fourier Transform (STFT), which can also be used to analyze non

stationary signals. While STFT gives a constant resolution at all

frequencies, the Wavelet Transform uses multi-resolution technique by

which different frequencies are analyzed with different resolutions.

A wave is an oscillating function of time or space and is periodic. In

contrast, wavelets are localized waves. The figure shown below gives

the demonstration of a wave and a wavelet. They have their energy

concentrated in time or space and are suited to analysis of transient

signals. While Fourier Transform and STFT use waves to analyze

signals, the Wavelet Transform uses wavelets of finite energy.

Demonstration of Wave

15

Demonstration of Wavelet

The wavelet analysis is done similar to the STFT analysis. The signal to

be analyzed is multiplied with a wavelet function just as it is multiplied

with a window function in STFT, and then the transform is computed for

each segment generated. However, unlike STFT, in Wavelet Transform,

the width of the wavelet function changes with each spectral component.

The Wavelet Transform, at high frequencies, gives good time resolution

and poor frequency resolution, while at low frequencies the Wavelet

Transform gives good frequency resolution and poor time resolution.

The Wavelet Series is just a sampled version of CWT and its

computation may consume significant amount of time and resources,

depending on the resolution required. The Discrete Wavelet Transform

(DWT), which is based on sub-band coding is found to yield a fast

computation of Wavelet Transform. It is easy to implement and reduces

the computation time and resources required. In CWT, the signals are

analyzed using a set of basis functions which relate to each other by

simple scaling and translation. In the case of DWT, a time-scale

representation of the digital signal is obtained using digital filtering

techniques. The signal to be analyzed is passed through filters with

different cutoff frequencies at different scales

16

One Dimensional Discrete Wavelet Transform Two-Channel Perfect Reconstruction Filter Bank: The analysis filter bank decomposes the input signal x (n) into two sub

band signals, L (n) and H (n). The signal L (n) represents the low

frequency (coarse) part of x (n), while the signal H (n) represents the

high frequency (or detail) part of x (n). The analysis filter bank first

filters x (n) using a low pass and a high pass filter. We denote the low

pass filter by af1 (analysis filter 1) and the high pass filter by af2

(analysis filter 2). As shown in the figure 3.2, the output of each filter is

then down-sampled by 2 to obtain the two sub band signals, L (n) and

H (n).

Reconstruction filter band

The synthesis filter bank combines the two sub band signals L (n) and

H (n) to obtain a single signal y(n). The synthesis filter bank first up-

samples each of the two sub band signals. The signals are then filtered

using a low pass and a high pass filter. We denote the low pass filter by

sf1 (synthesis filter 1) and the high pass filter by sf2 (synthesis filter 2).

The signals are then added together to obtain the signal y (n). If the four

filters are designed so as to guarantee that the output signal y (n) equals

the input signal x (n), then the filters are said to satisfy the perfect

reconstruction condition.

17

Assume the input signal x (n) is of length N. For convenience, we will

like the sub-band signals L (n) and H (n) to each be of length N/2.

However, these sub-band signals will exceed this length by L1/2, where

L1 is the length of the analysis filters. To avoid this excessive length, the

last L1/2 samples of each sub-band signal is added to the first L1/2

samples. This procedure (periodic extension) can create undesirable

artifacts at the beginning and end of the sub-band signals, however, it is

the most convenient solution. When the analysis and synthesis filters are

exactly symmetric, a different procedure (symmetric extension) can be

used, that avoids the artifacts associated with periodic extension. A

second detail also arises in the implementation of the perfect

reconstruction filter bank. If all four filters are causal, then the output

signal y(n) will be a translated (or circularly shifted) version of x(n). To

avoid this, we perform a circular shift operation in both the analysis and

synthesis filter banks.

Discrete Wavelet Transform (Iterated Filter Banks)

The Discrete Wavelet Transform (DWT) gives a multiscale

representation of a signal x(n). The DWT is implemented by iterating

the 2-channel analysis filter bank described above. Specially, the DWT

of a signal is obtained by recursively applying the lowpass/highpass

frequency decomposition to the lowpass output as illustrated in the

diagram 3.3. The diagram illustrates a 3-scale DWT. The DWT of the

signal x is the collection of subband signals. The inverse DWT is

obtained by iteratively applying the synthesis filter bank.

18

2-Dimensional Discrete Wavelet Transform

2-D Filter Banks

To use the wavelet transform for image processing we must implement a

2D version of the analysis and synthesis filter banks. In the 2D case, the

1D analysis filter bank is first applied to the columns of the image and

then applied to the rows. If the image has M rows and N columns, then

after applying the 1D analysis filter bank to each column we have two

sub-band images, each having M/2 rows and N columns; after applying

the 1D analysis filter bank to each row of both of the two sub-band

images, we have four sub-band images, each having M/2 rows and N/2

columns. This is illustrated in the diagram below. The 2D synthesis filter

bank combines the four sub-band images to obtain the original image of

size M by N.

One stage in multi-resolution wavelet decomposition of an image

2D Discrete Wavelets

Like in the 1D case, the 2D discrete wavelet transform of a signal x is

implemented by iterating the 2D analysis filter bank on the low pass

19

sub-band image. In this case, at each scale there are three sub-bands

instead of one.

There are three wavelets associated with the 2D wavelet transform. The

following figure illustrates three wavelets as gray scale images.

Note that the first two wavelets are oriented in the vertical and

horizontal directions; however, the third wavelet does not have a

dominant orientation. The third wavelet mixes two diagonal orientations,

which gives rise to the checkerboard artifact.

Advantages of Discrete wavelet transform

1. It gives information about both time and frequency of the signal.

2. Transform of a non stationary signal is efficiently obtained.

3. Reduces the size without losing much of resolution

4. Reduces redundancy.

5. Reduces computational time

Disadvantages of DWT:

1. Lack of shift invariance.

2. Lack of directional selectivity for higher dimensionality.

3. Unsatisfactory reconstruction.

4. It has more redundancy compare to DTCWT

20

The first two phases a) Image acquisition and b)Preprocessing are same

as in algorithm1but extracting features is done after applying 2-D DWT

on preprocessed image and the images are stored in database and test

database and matching has to be done.

Based on this, performance parameters have to calculated and results are

plotted.

2-D DWT gives four compressed band namely LL, LH, HL and HH.

Among these LL band is preferred since it gives approximate and better

results compared to LH, HL and HH bands. Haar wavelet is being used

in DWT.

Database Face images Text face images

Resize (128*128)

Discrete Wavelet Transform

LL Sub band

Co-efficient vector

Euclidian Distance classifier

Face image match/non match

21

Implementation with matlab: 2-D Dwt can be calculated in matlab

using an inbuilt command by choosing required wavelet/ wave filter.

In this test case2-D Dwt is calculated using ‘haar’ wavelet for JAFFE

Database choosing No of persons in database=7;

No of persons in test database=10;

No of images of each person in database=15; Test image=19

FRR versus Threshold

22

FAR versus Threshold

TSR versus Threshold

23

The EER is 0.200000 corresponding to the optimum threshold of

0.432602

The TSR is 0.800000 corresponding to the optimum threshold of

0.432602

24

DATABASE: JAFFE No. of

persons in

database

No. of

persons

out of

database

Number

of images

of each

person

Number

of the test

image

Optimum

threshold

2-D DWT

EER TSR

5 5 15 17 0.408196 0.100000 0.900000

5 5 15 18 0.383855 0.000000 1.000000

5 5 15 19 0.429051 0.200000 0.800000

5 5 15 20 0.432277 0.200000 0.800000

7 3 15 17 0.428339 0.000000 1.000000

7 3 15 18 0.431603 0.000000 1. 000000

7 3 15 19 0.432602 0.200000 0.800000

7 3 15 20 0.462472 0.142857 0.857143

6 4 15 17 0.426365 0.000000 1.000000

6 4 15 18 0.414367 0.000000 1.000000

6 4 15 19 0.432112 0.166667 0.833333

6 4 15 20 0.435446 0.166667 0.833333

7 3 12 20 0.473635 0.142857 0.857143

7 3 12 13 0.429423 0.000000 1.000000

7 3 12 15 0.450294 0.000000 1.000000

7 3 12 16 0.471678 0.000000 1.000000

7 3 12 18 0.431603 0.000000 1.000000

7 3 12 17 0.431552 0.100000 0.900000

7 3 12 19 0.441888 0.142857 0.857143

By varying the number of persons in database, number of images of each

person and test images, the values of EER and TSR are being calculated.

EER is the point of intersection of FAR and FRR at optimal

threshold, the lower the value of EER, the better is the algorithm

Also higher TSR value at EER shows how adaptable and robust

the algorithm is.

Creating a database and test database with images is demonstrated by

an example, consisting of 3 persons each with 8 images in database

and test database with 9th

image of 4 persons among which 3 are in

database and 1 out of database.

25

JAFFE Database: Images in database

Person 1:

Person 2:

Person 3:

26

Test database:

RESULTS ON DIFFERENT DATABASES:

ORL-FACES

No. of

persons

in

database

No. of

persons

out of

database

Number

of

images

of each

person

Number

of the

test

image

Optimum

threshold

EER TSR

20 20 8 10 0.403162 0.050000 0.950000

20 20 8 9 0.440096 0.100000 0.900000

20 20 7 8 0.426077 0.100000 0.900000

20 20 7 9 0.440096 0.100000 0.900000

20 20 7 10 0.397508 0.050000 0.950000

25 15 7 8 0.392461 0.123077 0.876923

25 15 7 9 0.404316 0.133333 0.866667

25 15 7 10 0.387847 0.075000 0.925000

30 10 7 10 0.371744 0.066667 0.933333

30 10 7 9 0.373902 0.183333 0.816667

30 10 7 8 0.388620 0.114286 0.885714

27

Database: Indian females

No. of

persons

in

database

No. of

persons

out of

database

Number

of

images

of each

person

Number

of the

test

image

Optimum

threshold

EER TSR

10 12 8 10 0.336653 0.100000 0.900000

10 12 8 9 0.305086 0.000000 1.000000

10 12 8 11 0.306075 0.100000 0.900000

12 10 8 10 0.338860 0.166667 0.833333

12 10 8 9 0.305086 0.000000 1.000000

12 10 8 11 0.275736 0.090909 0.909091

15 7 7 8 0.283907 0.133333 0.866667

15 7 7 9 0.314187 0.136364 0.863636

15 7 7 10 0.320892 0.133333 0.866667

15 7 7 11 0.349712 0.133333 0.866667

12 10 7 8 0.282989 0.083333 0.916667

12 10 7 9 0.313031 0.083333 0.916667

12 10 7 10 0.338860 0.166667 0.833333

12 10 7 11 0.307584 0.136364 0.863636

ORL_Faces database: It has been observed the same algorithm gives

EER values in the range of 0.183333 to 0.05000 and TSR values in

range of 0.816667 to 0.950000.

Indian Females database: It has been observed the same algorithm

gives EER values in the range of 0.166667 to 0.00000 and TSR values in

range of 0.833333 to 1.00000.

28

Indian Males

No. of

persons

in

database

No. of

persons

out of

database

Number

of images

of each

person

Number

of the test

image

Optimum

threshold

EER TSR

10 10 7 8 0.355908 0.100000 0.900000

10 10 7 9 0.378676 0.300000 0.700000

10 10 6 9 0.382884 0.250000 0.750000

10 10 6 8 0.373269 0.200000 0.800000

10 10 6 7 0.410954 0.300000 0.700000

15 5 7 8 0.285325 0.600000 0.400000

15 5 7 9 0.424989 0.500000 0.500000

12 8 7 8 0.318291 0.083333 0.916667

12 8 7 9 0.326656 0.431818 0.568182

15 5 5 6 0.325532 0.466667 0.533333

15 5 5 7 0.351491 0.457143 0.542857

15 5 5 8 0.286484 0.600000 0.400000

15 5 5 9 0.305127 0.500000 0.500000

The same algorithm is being applied on different databases such as

ORL_Faces, Indian Male and Indian female. Better results are observed

in case of ORL_Faces and not so good results on Indian Males.

Improvement of the algorithm can be done by applying more levels of

DWT or using advanced techniques like DTCWT i.e. Dual Tree

Continuous Wavelet Transform and Complex DTCWT.

29

The following table gives the results of a test case when DWT is applied

using different filters are used such as haar, Daubechies, Coiflets,

Symlets and Discrete Meyer. The values of some of the filters are

similar and that is due to the similarity in the wave function of the

wavelet. The feature size of the DWT may differ for different wavelet

like symlets give feature size of 129 whereas all Daubechies give

feature size as half of the preprocessed image size

Wavelet

filter

No. of

persons

in

database

No. of

persons

out of

database

Number

of

images

of each

person

Number

of the

test

image

Optimum

threshold

EER TSR

‘Haar’

or ‘db1’

7 3 12 17 0.431552 0.100000 0.900000

‘db2’ 7 3 12 17 0.439712 0.142857 0.857143

‘db3’ 7 3 12 17 0.443662 0.100000 0.900000

‘db5’ 7 3 12 17 0.444544 0.142857 0.857143

‘db10’ 7 3 12 17 0.461352 0.100000 0.900000

'coif1 7 3 12 17 0.443977 0.142857 0.857143

'sym2' 7 3 12 17 0.439712 0.142857 0.857143

'dmey' 7 3 12 17 0.770870 0.000000 1.000000

30

These are the results obtained when different bands such as LL, LH, HL

and HH bands are considered for feature extraction and matching. It has

been observed that better results are obtained for LL i.e. low-low band

which is generally used and good results for HL i.e. High-Low band.

The above plotted table is by considering HL band

No. of

persons

in

database

No. of

persons

out of

database

Number

of

images

of each

person

Number

of the

test

image

EER TSR

5 5 15 17 0.000000 1.000000

5 5 15 18 0.100000 0.900000

5 5 15 19 0.200000 0.800000

5 5 15 20 0.200000 0.800000

7 3 15 17 0.000000 1.000000

7 3 15 18 0.142857 0.857143

7 3 15 19 0.333333 0.666667

7 3 15 20 0.142857 0.857143

6 4 15 17 0.000000 1.000000

6 4 15 18 0.000000 1.000000

6 4 15 19 0.250000 0.750000

6 4 15 20 0.166667 0.833333

7 3 12 20 0.285714 0.714286

7 3 12 13 0.100000 0.900000

7 3 12 15 0.200000 0.800000

7 3 12 16 0.142857 0.857143

7 3 12 18 0.142857 0.857143

7 3 12 17 0.000000 1.000000

7 3 12 19 0.333333 0.666667

31

REFERENCES

1. W. Zhao, R. Chellappa, P.J. Phillips and A. Rosenfeld,

“Face recognition: A literature survey,” ACM Comput.

Surv.,vol. 35, no. 4, pp 399–458 (2003).

2. Digital Image Processing Using MATLAB 2nd Ed. by

Gonzalez, Woods, and Eddin © 2009

3. Second edition, Digital Signal Processing using Matlab and

Wavelet Micheal Week, Georgia state university

4. Ramesh K , KB Raja, “Gram-schmidt orthoganalisation

based face recognition using DWT”, proceedings of

International Journal of engineering science and Technology

(IJEST), vol.3,No.1,Jan 2011.

5. N.V.S.L. Swarupa, D.Supriya, “face recognition system”,

Proceedings of International Journal of computer applications,

vol.1,No.29,2010.

6. Sulochana Sonkamble, Dr. Ravindra Thool, Balwant

sonkamble , “the survey of biometric recognition system and

their applications”, proceedings of Journal of Theoratical and

applied information Technology, 2005-2010.

7. http://en.wikipedia.org/wiki/DWT

8. http://en.wikipedia.org/wiki/Biometrics