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WAES 3104:

Fundamentals of Artificial

Neural Network



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#ntroduction

The human face plays an important role in our social interaction, conveyingpeople’s identity. Using the human face as a key to security, biometric face

recognition technology has received significant attention in the past several

years due to its potential for a wide variety of applications in both law

enforcement and non-law enforcement.

As compared with other biometrics systems using fingerprint/palmprint and

iris, face recognition has distinct advantages because of its non-contact

process. ace images can be captured from a distance without touching the

person being identified, and the identification does not re!uire interacting withthe person. "n addition, face recognition serves the crime deterrent purpose

because face images that have been recorded and archived can later help

identify a person.

Automated facial recognition involves the identification of an individual based

on his or her facial geometry. or facial recognition to be successful, there

needs to be a !uality digital image of an individual’s face, a database of digital

images of identified individuals, and facial recognition software that will

accurately find a match between the two.

#f all biometric technologies, facial recognition most closely mimics how

people identify others$ by scrutini%ing their face. &hat is an effortless skill in

humans has proven immensely difficult and e'pensive to replicate in

machines. (ut through a convergence of factors in the past few years, facial

recognition has become increasingly accurate technology.

)igital images have become a trend, through the e'istence of surveillancecameras, camera-e!uipped smart phones, and ine'pensive high-!uality digital

cameras. *heap data storage has led to massive online databases of images

of identified individuals, such as licensed drivers, passport holders, employee

")s and convicted criminals. "ndividuals have embraced online photo sharing

and photo tagging on platforms such as acebook, "nstagram, +icasa and

lickr. There have also been significant improvements in facial recognition

technology, including advancements in analy%ing images and e'tracting data.

aces have been transformed into electronic information that can be analy%ed

and categori%ed in unprecedented ways. &hat makes facial image data so

valuable, and so sensitive, is that it is a uni!uely measurable characteristic of



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ome security applications of facial recognition technology are undoubtedly

beneficial, such as authentication of employees allowed to access nuclear

plant facilities, for e'ample. #ther e'ample is in the presidential election,

the e'ican government employed facial recognition software to preventvoter fraud. ome individuals had been registering to vote under several

different names, in an attempt to place multiple votes. (y comparing new

facial images to those already in the voter database, authorities were able to

reduce duplicate registrations. At the same time, facial recognition holds

implications for privacy and for societal values in general.

921 Scoe



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&ith the e'panded use of video surveillance cameras and camera

e!uipped mobile phones throughout the world, we are seeing increasing

rate of available crime scene video/still photo evidence that contains

suspect face images.

0aw enforcement agencies can now use these crime scene latent faceimages to identify possible persons of interest in these criminal

investigations by the use of the face recognition system. This system will

focus on face recognition in criminal investigations

"t can allows the enhancement of images for comparison to the gallery.

This allows operators to develop watch lists of potential matches while

maintaining a full audit trail for each step in the process. "t also helps

investigators identify individuals in crime scene photos and surveillance

videos by matching facial images against the agency1s mugshot repository.

2'aminer also provides a set of inspection tools that helps identify theperson in !uestion in a timely manner, allowing investigators to act upon

the search results in the critical time period after a crime has been

committed.

2'aminer enhances crime scene video face images for interlaced video,

poor lighting, motion blurriness, low resolution, aspect ratio inconsistencies

and off angle poses. The enhanced images are then searched against the

full ugshot 3epository with possible demographic filtering and candidate

lists created.

The system also contains the face search image verification tools 4side5

by-side split screens with interactive image viewing controls, key face

landmark feature angle and distance measurement tool6 necessary for the

e'aminer to compare the search and candidate list face images to verify

investigation results.

929 (imitations and Assumtions

3esearch has shown that it is surprisingly difficult to match a face to an

image This routine task performed hundreds of times every day by



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ecurity guards and police officers, turns out to be highly error prone.

"n one of the e'periment of this phenomenon a group of researcher did a

field test to establish the level of fraud protection afforded by the inclusion

of ") photos on credit cards. upermarket checkout staff were re!uired to

validate the photo-credit cards by deciding whether or not the photographwas of the person presenting the card. 2ven though the staff were aware

they were taking part in a study concerning the utility of photo credit cards,

they performed surprisingly badly, with about half of the fraudulent cards

being accepted, and about one in 7 of the valid cards being falsely

re8ected.

9ere are some of the limitation or problems found in the system $

a6 The resolution of the video images are not sufficient for facial

recognition algorithmsb6 The data elements are not marked correctly on the sample datac6 The lighting and sub8ect position in the video lend itself to photographic

issues such as perspective distortions, angle of pose issues, etc.d6 tatistical error analysis are missing in the e'pert witness work.e6 The defendant e'pert is not an :e'pert; in photography and +hoto-

anthropometric area.f6 The data points used are not reliable points.

9ence, we could assume that that every criminals will have to go through a

series of high !uality 1mugshot’ photo shoot to compare with its own

database. econd assumption is that the images captured will be convertedinto grayscale images. This will help the system to overcome the risk of the

criminals changing their appearance.

32 *aracteristics

acial recognition system is based on the ability to recogni%e a face and then

measure the various features of the face. 2very face has numerous,

distinguishable landmarks, the different peaks and valleys that make up



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facial features. These landmarks defined as nodal oints. 2ach human face

has appro'imately < nodal points. The features measured by the system are$

• )istance between the eyes

• &idth of the nose

•

)epth of the eye sockets• The shape of the cheekbones

• The length of the 8aw line

These nodal points are measured creating a numerical code, called

a facerint, representing the face in the database. (ut this system will focus

on eyes.

The eye, as in the iris, is the part of the face that is hard to change. or

e'ample, part of face such as the nose, ears, shape of the face or even the

skin color could be changed by undergoing cosmetic surgery. (y undergoingthis procedure, these criminals would be able to fool the system if the

proposed system focuses on these features, e'cept for the iris. The criminal

could wear contact lenses to change their colour of their eye, but as we are a

step ahead by doing the recognition in grayscale, the system will be able to

recognise the criminal regardless of the colour of the eye.

.The distance between the eyes 4iris6 is able to increase the accuracy of the

system. &hen the system is retrieving the image, it will automatically measure

the distance between the irises, and will try to match with the database. "f

match is found, added with the match of right or left eye, the system will notifythe law enforcement.

42 Suitable ANN for face reco%nition s;stem

Artificial neural network is to be designed and trained to recogni%e the

f th d t b A i i t th t t h f i



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centered in the system’s field of vision is available.9owever the imaging

system may not be perfect and the faces may suffer from noise.

+erfect classification of ideal input images is re!uired, and reasonably

accurate classification of noisy images. 9ence some basic neural network

modelsmust be applied to each models in modules of face recognition system.

or face detection module, a three-layer feed forward artificial neural network

can be used detect human faces so that face detecting rate is rather high.

or face alignment module, a multilayer perceptron 40+6 with linear function

4three-layer6 is used, and it creates ) local te'ture model for the active

shape model 4A6 local searching. or feature e'traction module, a method

for combination of geometric feature-based method and "*A method in facial

feature e'traction is used. or

face matching, a model which combines many artificial neural networks

applied for geometric features classification is used.

21 Face reco%nition alication

2121 #nut



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To create a neural and train the system, images from the will be used. To

do this the network will first be trained on ideal images until it has a low

sum-s!uared error. Then the network will be trained on < sets of ideal

images. All training is done using back propagation with both adaptive

learning rate and momentum. All training sets are in grayscale to increase

accuracy. 9ere are some e'amples of the images of a person$

All the images are not the similar even though these picture are of the same

person. (y doing this, the system will be able to adapt if there might be a

slight changes if the system is trying to recogni%e the same person but maybe

from a different angle. The position of the iris of the person is also varies as

this will help to train the system. The training e'ample will also include person

with different features such as wearing spectacles, have facial hair, moles andmany more. This will help to train the system in better ways for detail match

inspection.

This will provides toolset for comparing the match results conveniently in

ranked order to confirm the specific match. +robe and reference images can

be e'amined in different views allowing for a more comprehensive match

confirmation.

or e'ample$

The image to the right displays the side by side image verification screen with

the angle and distance measurement tool from 2'aminer used to track the



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(elow here is how the system read the image through atlab.

= read images for i > from$length

data7?i-@ > imread4BC*$DimagesDf7DC numstr4i6 C.tifCE6F data7?i-@ >

imnoise4data7?i-@,Csalt G pepperC,u6F

data7?i-@ > imad8ust4data7?i-@,Blowin highinE,Blowout highoutE6F endF

(elow is the system’s set epoch value

=-------------------DDDTrain test set///----------------

hd>7F

lr>.HF

ep>F

load wintfaceFload %intfaceF &I>winitFJI>%initF

save w! &I F save %! JIF



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=--------------------------

To avoid increased computation and long training times, it is necessary to

resi%e the face images.

Therefore the image is resi%edKLtopi'elsL immediately after

they have passed through the hit and miss kernels - that is, before enteringthe classification stage. ince each sub8ect has an e!ual number of @ face

images in the training set and the test set, their input dimension to the system

is theKMsame$columns@rowspi'el units. 2ach sub8ect is trained

individually

*ode that will resi%e the pictures.

=-------------------DDD3esi%e///----------------

re% > LF

for i > from$length

temp > imread4BC*$DimagesDf7DC numstr4i6 C.tifCE6F temp > imresi%e4temp, Bre%

re%E6F

data74i,$6 > temp4$6F endF

=--------------------------

irst step is to run and train all the training set.



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Then, second step is we train the test set

Interface



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2129 /rocess

After the system has been trained we can start process the images to be

detected.

for e'ample if we choose no N$



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here is the detected image with recognition accuracy > M=



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+rocess on to train the eyes on the training set 4Training et6

4Oalidation et6

train eye using 4test set6



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2123 )utut

The pre-recognition and actual recognition procedures are the same as for face images,

e'cept that when an eye is detected, the system will retrieve the face to which that

detected eye belongs, together with the face from which the test eye image is cropped.

Assume that the system now have a detected face. The ne't step is to use the cropped

eye images to detect the eyes of the detected face. #n this step, the detected face

image is loaded and convolution is performed using the cropped pair of left and right

eyes as templates. The detected eye region is indicated by a TA+ 4target aim point6rectangle that surrounds it.

Pe't, the system will locate the eye level using 9ough Transform. Then, the system will

shift the TA+ rectangles to the correct position as final ad8ustments are made. The

output images are given as follow.

The system then will calculate the euclian’s distance between the eyes.



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This figure shows the basic process on how the process of image recognition has been

done using artificial neural network.



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"2 *onclusion

ace recognition is challenging problems and there is still a lot of work that needs to be

done in this area. #ver the years, face recognition has received substantial attention

from researchers in biometrics, pattern recognition, computer vision, and cognitive

psychology communities.This common interest in facial recognition technology among

researchers working in diverse fields is motivated both by the remarkable ability to

recogni%e people and by the increased attention being devoted to security applications.

Applications of face recognition can be found in security, tracking, multimedia, and

entertainment domains.

&e have demonstrated how a face recognition system can be designed by artificial

neural network. Pote that the training process did not consist of a single call to a training

function. "nstead, the network was trained several times on various input such as image

of the same person that varies like different angles, face features4bear,spectacles and

many more6.

urther work can be e'panded in many ways.The algorithm can be e'tended to include

other recognition procedures, such as detectin% faces from a %rou oto%ra or

faces in motion. "n addition of this work, we can use &ree<dimensional face

reco%nition 4L) face recognition6 instead of ) images which is a modality of facial

recognition methods in which the three-dimensional geometry of the human face is used

which can increase the system accuracy.



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2 +eferences

7. http$//www.nec.com/en/global/solutions/security/technologies/faceQrecognition

.html

http$//ubi!uity.acm.org/article.cfmRid>M<HNH

http$//www.priv.gc.ca/information/research-recherche/7L/frQ7LLQe.asp

http$//www.fbi.gov/about-us/c8is/fingerprintsQbiometrics/biometric-center-of-e'cellence/modalities/facial-recognition

http$//en.wikipedia.org/wiki/acialQrecognitionQsystem

http://www.nec.com/en/global/solutions/security/technologies/face_recognition.html


http://ubiquity.acm.org/article.cfm?id=985625

http://www.priv.gc.ca/information/research-recherche/2013/fr_201303_e.asp

http://www.fbi.gov/about-us/cjis/fingerprints_biometrics/biometric-center-of-excellence/modalities/facial-recognition


http://en.wikipedia.org/wiki/Facial_recognition_system

http://ubiquity.acm.org/article.cfm?id=985625

http://www.priv.gc.ca/information/research-recherche/2013/fr_201303_e.asp



http://en.wikipedia.org/wiki/Facial_recognition_system



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