Investigation of Facial Artifacts on Face Biometrics … · Investigation of Facial Artifacts on...

Investigation of Facial Artifacts on Face Biometricsusing Eigenface based Single and Multiple Neural Networks

K. SundarajUniversity Malaysia Perlis (UniMAP)School of Mechatronics Engineering

02600 Jejawi - PerlisMALAYSIA

[email protected]

Abstract: - Biometrics has been an important issue pertaining to security in the last few decades. Departments oragencies entrusted with national security are increasingly installing surveillance cameras in strategic or criticalareas to monitor the identities of the general public. Upon locating suspicious characters in the video feed, they arecompared with existing databases to find a match. These databases are generally compiled from the NationalRegistration Department (NRD), Immigration, intelligence agencies, etc. Unfortunately, as mentioned in mostreports of tragic events, suspicious characters do not resemble anything like what has been stored in the databases.There is a high chance that the face biometric identification software will miss these culprits. In this paper wepropose to investigate the effects of facial artifacts on the recognition rate of eigenface based neural networks. Ithas been found that eigenfaces coupled with Euclidean distance can be successfully used to recognize the humanface in almost real-time. However, facial artifacts can cause the features that characterize a face to be distorted.Hence, it is desirable to identify problematic facial artifacts that can cause false identification or no identification.The main focus of this paper is the investigation of common facial artifacts on the performance of recognition andthe proposition of modification to existing databases to improve the positive rate of identification. A professionalgraphic artist was used to modify the images used in the experiments. We use a single and multiple eigenface basedneural network as the classifier in our experiments.

Key-Words: - Face Biometrics, Face Recognition, Eigenfaces, Facial Artifacts.

1 IntroductionBiometrics consists of automated methods ofrecognizing a person based on a physiological orbehavioral characteristic. Among the features that aremeasured are face recognition, fingerprint, handgeometry, handwriting, irises and voice patterns.Biometric technologies are becoming the foundation ofan extensive array of highly secure identification andpersonal verification solutions. As the level of securitybreaches and transaction fraud increases, the need forhighly secure identification and personal verificationtechnologies is becoming apparent. Biometric basedsolutions are able to provide for confidential financialtransactions and personal data privacy. The need forbiometrics can be found in federal, state and localgovernments, in the military and in commercialapplications. Enterprise-wide network securityinfrastructures, government IDs, secure electronicbanking, investing and other financial transactions,retail sales, law enforcement and health, social servicesare already benefiting from these technologies.

Face recognition for biometric identification orface biometrics is a fairly young technology comparedto other biometrics. Research in this field has beengoing on for decades, but it has been in the last 10 to15 years that the greatest advances have taken place.Reviews of face recognition methods can be easilyfound in the literature such as [1] [2] and [3]. Owing tothe growing number of applications, especially in thesecurity domain, various research groups have devotedtheir work to face biometrics. In the past two decadesor so, various methods (2D and 3D) have been appliedand benchmarked for face biometrics. Some of themost important results are found in 2D face biometrics.[4] and [5] used principle component analysis (PCA)to recognize faces. In [6], [7] and [8] artificial neuralnetworks (ANN) have been used. A localautocorrelation coefficient (LAC) method was found tobe computationally efficient, invariant to translationand suitable for pattern recognition in [9]. Thisapproach was applied by the authors in [10] and [11]

WSEAS TRANSACTIONS on SYSTEMS K. Sundaraj

ISSN: 1109-2777 127 Issue 1, Volume 8, January 2009

for pattern recognition and found to produce goodresults. In [12] the authors applied a linear discriminantanalysis (LDA) to process human faces. This methodwas extended by the inclusion of LAC to perform facerecognition with a high successful rate. There was animprovement in performance but at the cost of memoryspace. In [13] and [14], the Support Vector Machine(SVM) technique was applied to face recognition and[15] applied a method called Elastic Graph Matching(EGM) to face biometrics. PCA couple withautocorrelation feature vector (AFV) was used by [16]to speed up the recognition process. 3D face biometricsis relatively new. Very few researchers have beeninvolved in this area. By 3D face biometrics, we meanthe combination of a 2D face image plus a 3D disparityimage. The disparity image is usually derived from theuse of stereovision images. According to [17], adding3D face biometrics can overcome some of theshortcomings of 2D face biometrics and improverecognition rate. Some of the recent works on 3D facebiometrics using stereovision images comes from [18],[19] and [20].

However, face biometrics for security can be acomplicated issue. We are not talking anymore abouthow accurate a face biometric system is but we wish toknow how much foolproof it can be. Security agentsresponsible for national security rely heavily on facebiometric softwares to identify culprits. Very often, asit was reported in the 911 terrorist attacks, had theresponsible culprits been identified earlier at theairports, that tragic incident could have been avoidedaltogether [21]. 28 countries in the European Union(EU) have agreed to put in place a face readingsecurity system by the end of 2007. In fact, the UnitedKingdom (UK) is already testing its face biometricsystem at some airports [22]. The fact that facebiometric systems could be used to save lives has madethe notion of a foolproof system all the more relevant.A foolproof system is one that cannot be fooled or onethat knows when it is fooled [23]. Face images ofperpetrators in the database can be considered as thestandard image without facial artifacts. An individualwho wishes to carry out a plan undetected and who isaware of the presence of any face biometric system(most of them are) would naturally opt to maskhimself/herself with an aid of facial artifacts [24].Some common facial artifacts are shown in Fig. 1. Thepreviously mentioned 2D and 3D face biometricsystems do not consider the problem of how much thesystem can be fooled but rather how accurate is thesystem in matching. Most of these systems insist

conditions such as high resolution, full-frontalperspective, sufficient contrast and good lighting.Other acquisition conditions are also required,such as a neutral facial expression and the removalof any glasses, headgear or hair that obscuresfacial landmarks [25] [26].

(a) Colored Iris (b) Rimmed Glasses

(c) Puffed Lips (d) Cap or Hat

(e) Broadened Nose (f) Tissue Scare

(g) Thickened orDiminishing Eyebrows

(h) Moustache

(i) Freckles (j) Thick Beard

Figure 1. Some common facial artifacts.

This paper presents an investigation into theproblems posed by facial artifacts in a face biometricsystem. We limit ourselves to systems that performface recognition using eigenface based neuralnetworks. The method of using eigenfaces is not new.In [27] and [28], a near real-time system for facebiometrics was developed using such a method. We



employ such a system in our analysis. Experimentswere done to obtain the fool rate when facial artifactswere added to or removed from the standard faceimage. In the case, when the facial artifacts could notbe removed or added easily onto a person in real, aprofessional graphic artist was used to touch up thestandard image accordingly. We compare resultsobtained by using eigenfaces coupled with minimumdistance and eigenface based back-propagation neuralnetwork.

2 The Eigenface MethodThe basic idea of eigenfaces is that all face images aresimilar in certain aspects. There is a common pattern inall faces; for example, a face has two eyes, one nose,one mouth, etc. These characteristic features arecalled eigenfaces or the principle components inthe facial recognition domain. They can beextracted out of the original image data by meansof a mathematical tool called PrincipalComponent Analysis (PCA). With this tool, animage of a human face can be transformed intoeigenfaces. It is interesting to note that the originalimage can be obtained by combining all theeigenfaces. Therefore one could say that theoriginal face image can be reconstructed fromeigenfaces if one adds up all the eigenfaces. Eacheigenface represents only certain features of theface. If the feature is present in the original imagevery often, the weight of the correspondingeigenface should be greater. On the contrary, if theparticular feature is not (or almost not) found inthe original image, then the correspondingeigenface should have a smaller (or zero) weight.So, in order to reconstruct the original image fromthe eigenfaces, one has to build a weighted sum ofall eigenfaces. That is, the reconstructed originalimage is equal to a sum of all eigenfaces, witheach eigenface having a certain weight. Thisweight specifies, to what degree the specificfeature (eigenface) is present in the originalimage.

The procedure of applying the eigenfacemethod to the face biometric problem is as follows[29] :

a) We start with the training sets of images 1, 2,…, m with each image denoted as I(x,y) where xand y are the size of the image in rows andcolumns.

b) Convert each image into its gray scale values andcrop them into an image of size n n. Ensure thatall faces in these cropped images are located at thesame position of the image, for example, in thecenter of the image. Also, n has to be fixed for allimages.

c) Form a set of vector [n2 1]. Combine theensemble of vectors into a new full-size matrix [n2

1 m] (row column depth) where m is thenumber of training images.

d) Find the mean of the training set by,

m

iim 1

1 (1)

e) Compute the mean subtracted face in the trainingset using,

miii ,,2,1 (2)

f) Form the mean subtracted matrix A = [1, 2, …,m] of size [n2 m],

g) Compute the covariance matrix from A and itstranspose AT using,

mnTmnmm AAC 22 (3)

h) Find the eigenvectors mm and the eigenvalues m

from the C matrix using any standard numericalmethod [30].

i) The eigenfaces U, can now be obtained using,

mkUm

nknnk ,,2,1

1

(4)

j) Order the eigenvectors in decreasing eigenvaluesand normalize U. Instead of using all m eigenfaces,a subset of them m’, can be selected to representthe characteristic features of that particular training



set. This subset contains the strong features(highest eigenvalues) of the person. Based on thissubset, the weights that describe the characteristicsof the training class is given by,

',,2,1 mkUW kTkk (5)

k) The weights form a feature vector given as,

'21 ,,, mT WWW (6)

l) An approximated face can be reconstructed byusing its feature vector , and its eigenfaces Uusing,

',,2,1'

1

mkUm

kkkk

(7)

m) Train the single or multiple neural networks withthe feature vector, .

n) Perform classification with unknown featurevector, . Tabulate the recognition rate.

3 Classification and RecognitionThe feature vector obtained from the previous

section is used as inputs through a back-propagationneural network for classification and recognition. Theproposed procedure for this study is shown in Fig. 2. Inorder to determine the value of m’ used in Fig. 2, the meigenvectors are firstly ordered according to theireigenvalues. The eigenvectors with the largest m’eigenvalues are chosen among the m eigenvectors. Thevalue of m’ is determined by the following,

m

ii

m

ii

1

'

1 (10)

where = [1, 2, …, m] are the eigenvalues of the meigenvectors and is a used defined threshold thatrepresents the maximum percentage of variationallowed in the m’ eigenvectors.

Figure 2. Proposed experimental process.

In our experiments, a back-propagation neuralnetwork for face biometrics was implemented andtrained. A back propagation neural network consists ofan input layer of nodes to accept input variables, anoutput layer of nodes to output results. The number ofnodes on both ends depends on the number of inputand output variables. There exists also one or severallayers of nodes in between the input and output layerscalled hidden nodes. These nodes help capture thenonlinearity of the data. They can be fully or partiallyconnected between layers through the weights betweennodes. In the application of this back-propagationnetwork for face recognition, parameters such aslearning rate, types of transfer functions, momentum,number of hidden layers, number of hidden nodes, etc.have to be determined in advance. In ourimplementation as shown in Fig. 3, a combination ofTansig and Logsig functions has been used to obtain anonlinear sigmoid function. This most commonly usedtransfer function was chosen in the hidden and outputlayers. The nonlinear sigmoid function is given as,

xexf

11)( (8)

In the single and multiple architecture neural networks,the number of input nodes is a function of the

UnknownFeatureVector

FaceDatabase

TrainingSet

UnknownFace

mEigenfaces

Classifier Trainer

Neural Network

KnownFeatureVector



dimension of the feature vector, . On the other hand,the number of output nodes in the single architectureneural network is equal to the number of individuals(or classes) in the face data base whilst in the multiplearchitecture neural network, the number of outputnodes is always equal to one. The number of hiddenlayers was arbitrary; one was used in our experimentfor simplicity and efficiency. The number of nodes inthe hidden layer was set according to,

NodesOutput

NodesInput

NodesHidden

##21# (9)

However, the final parameters of the network and thenumber of hidden neurons were adjusted during thelearning phase based on the performance of thenetwork. Basically, this was a trial and error process.We also set the termination criteria of our network tobe 1000 epochs or when the sum of squared error(SSE) reached 0.1.

Figure 3. The architecture of the proposed neuralnetwork comprised of Tansig and Logsig neurons.

4 Implementation and ResultsThe proposed method was implemented on a personalcomputer with Pentium Core 2 Duo 2.8 GHz and 1GBSDRAM. Fig. 4 shows the software that wasdeveloped to capture the region of interest of a humanface. This software was developed using Visual Basic6.0 under the Windows XP platform. A Marlin FO33C

½progressive scan color CCD firewire camera with amaximum resolution of 640 480 pixels was used tocapture the image of the subject in various orientation.All images were converted to gray scale values usingthe internal driver of the hardware provided by themanufacturer. The distance of the subject from thecamera during the process of capturing face image wasadjusted such that a complete face (including hair andbeard) plus some background would fit into a windowsize of about 400 400 pixels. This choice is actuallyarbitrary and differs from one author to another in theliterature.

Figure 4. Software developed for the face acquisitionand face recognition using neural networks.

A total of 15 individuals or classes were usedas subjects in the experiment. Each individual’sface image was captured 12 times. In each set of12 images, 3 images were adjusted with facialartifacts by either touching up by a professionalgraphic artist, manual inclusion of artifacts beforeimage capture or by face morphing using adedicated software. These 3 images were onlyused in the recognition phase. In the remaining 9images, 1 image was captured as a standard image(full frontal view), 5 more images were normalface images in various orientation and finally 3more images of various expressions of emotion.These 9 images were used as the source of data inthe training phase. Fig. 5 shows a sample ofsubjects that can be considered to have faces withartifacts. Fig. 6 shows a sample of subjects thatwere used in the training process.



1(a) (b) (c)

2(d) (e) (f)

3(g) (h) (i)

4(j) (k) (l)

5(m) (n) (o)

Figure 5. Five sample of subjects with various types of facial artifacts that were obtained using touching up by aprofessional graphic artist, manual inclusion of artifacts before image capture or by face morphing using adedicated software.



(a-1) (a-2) (a-3) (a-4)

(b-1) (b-2) (b-3) (b-4)

(c-1) (c-2) (c-3) (c-4)

(d-1) (d-2) (d-3) (d-4)

(e-1) (e-2) (e-3) (e-4)

Figure 6. Sample of subjects that were used in the learning phase of the back propagation neural network. The leftmost images are the standard image of each subject. Also included are images of various face orientation andimages of faces of various emotions. Taken from [31].



To setup of the network, the eigenface method wasfirstly applied to the training set of images of eachsubject to find the best m’ eigenfaces. Table 1summarizes the result of this procedure. Then, in thelearning phase of the back propagation neural network,the corresponding feature vectors of the best m’eigenfaces of all subjects are used as inputs to train thesingle and multiple networks. The data is fed from theinput layer, through the hidden layer to the output layerwithout feedback. The network then uses the feedforward error back propagation scheme to search for asurface with minimum error using gradient descent.The process of feeding the input layer with data isrepeated until the network is self-adjusted with a set ofweight that gives minimum error. These weights arestored and are used later in the recognition process.Table 2 shows the network parameters for both thesingle and multiple architectures used in ourexperiments at the end of the training phase. Thenetwork parameters were adjusted until theperformance of the network was acceptable.

Table 1. Determination of the best m’ eigenfaces.

Parameter Value# Database 15 subjects

# Training Images (m) random 5 out of 9 per subject 0.8 (fixed)

m’ (per subject) 4 (final choice)

Table 2. Final parameters of the back propagationneural network after adjustment during training.

Architecture Parameter Value# input nodes 4

# output nodes 15# hidden nodes 12

Momentum 0.8r, Learning Rate 0.9

Single(one for all)

SSE 0.1# input nodes 4

# output nodes 1# hidden nodes 3

Momentum 0.7r, Learning Rate 0.9

# of Hidden Nodes 50

Multiple(per individual)

SSE 0.1

After the feature vector was obtained for variouscombinations of training images, the proposed networkwas set up and trained. The proposed method was thenvalidated for the recognition of faces with facialartifacts with different sizes of trained data (variouscombinations of trained faces). Recognition was doneusing 3 methods; eigenfaces with Euclidean minimumdistance (EEMD), eigenface based single neuralnetwork (ESNN) and eigenface based multiple neuralnetworks (EMNN). Fig. 7 shows the experimentalresults of these experiments. Fig. 8 shows the falseacceptance rate (FAR) which is the probability that thewrong person is accepted (fraud) and the falserejection rate (FRR) which is the probability that theright person is rejected using these methods. These twoparameters can serve as a measure of how foolproofthe face biometric system is.

From the results, we can see that the EEMDmethod gives most than 78% recognition rate for avery small size of data. But this gradually decreases asthe size of the database increases. A similar pattern canbe observed for both the neural networks; ESNN andEMNN. The recognition rates for all tested methodsare not as high as reported in the literature due to thefact that in the conducted experiments, facial artifactswere added. Facial artifacts were added one by one asthe database size was increased. This amounts toreducing the contribution of the feature vectors thatwas originally determined from the database. It is clearthat facial artifacts are capable of fooling a facebiometric system especially when the database sizebecomes large. On average, all the experimentedmethods gave a recognition rate of more than 75%.With the use of a single neural network, there is anincrease of about 5–10% in recognition rate. Whenmultiple neural networks are used, there is a furtherincrease of about 3–5% in recognition rate whencompared to a single network.

5 ConclusionsThis paper presents a study to investigate the effects offacial artifacts on the recognition rate of face biometricsystems that are based on eigenfaces. The proposedstudy is based on the implementation of a fast andefficient method to extract the eigenfaces from a set offace images that belong to an individual. These imagesare expected not to contain any facial artifacts. In therecognition phase, facial artifacts were added to theimages of an individual with the help of a professionalgraphic artist, manual inclusion or image morphing by



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itio

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Figure 7. Recognition rate of proposed study.

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FA

R+

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Figure 8. Foolproof rate of the proposed methods.

a dedicated software. The amount of facial artifactsadded was slowly increased as the database size wasincreased. According to our experiments, it was foundthat all methods could be fooled by the inclusion offacial artifacts especially when the database sizeincreases. We also found that eigenface based multipleneural networks produced a good recognition rate evenwith the presence of facial artifacts. The foolproof ratewhich has been presented is a combination of FAR andFRR. It is hoped that these rates can help securityagents that are in charge of national security decide thetype of method chosen as the discrimination basis ofthe face biometric system. A limitation in the proposedstudy is the size of the training database in terms ofindividuals. This is partly due to the fact thatmodifying images with facial artifacts can be timeconsuming to produce realistic images. It is hoped thata specific database will be created and available onlinefor this purpose in our future work.

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