FAKE FACE DATABASE AND PREPROCESSING

Jan Zizka (Eds) : CCSIT, SIPP, AISC, PDCTA - 2013
pp. 13–24, 2013. © CS & IT-CSCP 2013 DOI : 10.5121/csit.2013.3602
FAKE FACE DATABASE AND PRE-
PROCESSING
Aruni Singh, Sanjay Kumar Singh and Shrikant Tiwari
Department of Computer Engineering
IIT(BHU), Varanasi, India
arunisingh@rocketmail.com
sks.cse@itbhu.ac.in
shrikant.rs.cse@itbhu.ac.in
ABSTRACT
Face plays an ethical role in human interaction compared to other biometrics. It is most
popular non-intrusive and non-invasive biometrics whose image can easily be snapped without
user co-operation. That is why; criminals and imposters always try to tamper their facial
identity. Therefore, face tampering detection is one of the most important bottlenecks for
security, commercial and industrial orbit. Face tampering detection is one of the most important
bottlenecks for security, commercial and industrial orbit. In particular, few researchers have
addressed the challenges for the disguise detection but inadequacy is benchmark database in
public domain. This paper addresses these problems by preparing of three category of tampered
face database within the framework of FRT (Facial Recognition Technology) and evaluates the
performance of this database on face recognition algorithms. These categories of database are
dummy, colour imposed and masked face.
KEYWORDS
Dummy Face, colour imposed face, masked face, Facial Recognition Technology, tampering.
1. INTRODUCTION
Due to wide application of biometric technology in information security, law enforcement,
surveillance, and others, it plays a crucial role and attracts intensive interest from researchers for
personal authentication. Among all biometrics, face plays an ethical role in human interaction. It
is most popular non-intrusive and non-invasive whose image can easily be snapped without user
co-operation [2]. That is why; criminals and imposters always try to hide their face by means of
tampering. The sample image of cheating is shown in Fig.1. Therefore, face tampering detection
is highly desirable in security, commercial and industrial orbit.
Techniques by which the imposters cheat the authentication system by presenting the fake
biometric are known as spoofing. For security concern, research must be focus in the direction of
accurate classification of image of real face from the image of tampered face because criminals or
imposters always use different types of tampering mechanism for the concealment of their facial
identity. If captured image from scene is image of real face then it needs to go to FRT otherwise
switch towards other forensic techniques for imposter’s and criminal’s identification. It can be
said that without spoofing measurement the advancement in FRT is defenceless to attack.

14 Computer Science & Information Technology (CS & IT)
To develop any new methodologies or techniques, efficient and benchmark database is required.
Although so many databases are available in public domain for FRT, but to the best of our
knowledge, not even a single database of tampered face is available in public domain to test the
performance of face recognition algorithms and to discriminate the real face from tampered face.
For verification and validation of tampering detection methodologies, benchmark standard
database is essential. For this purpose we have prepared real and tampered both type of face
images of same subject.
Several research groups have built face databases with a lot of variations in poses, illuminations,
snapshot time, follow-up time etc. for evaluating and comparing the performance of the face
recognition algorithms. FERET database [12][13] contains 14051 eight-bit gray scale human face
with frontal, left and right profile views, and quarter left and right views of images including
variations in illumination and expression. It was created by FERET program, which ran from
1993 to 1997[14]. XM2VTS database [15][16] contains multimodal database of 295 subjects with
follow-up after four months including speaking head shots and rotating head shots [14]. It is a
huge video database containing wide range of pose angle variations. But it does not include any
information about the time acquisition parameters, such as illumination angles, illumination
colour or pose angle [14]. Yale B [17] contains gray face image of 15 subjects having 64 different
lighting angles and 9 different poses, variation in light and expression. The lighting variations are
as centred-light, left-light and right-light [14]. AR face database [18] contains colour image of
126 (70 males and 56 females) subjects having variation in illumination, expression, occlusion
under strictly controlled environment and total 4000 colour frontal view images. The images were
taken during two sessions. PIE Database [19][20] contains images if 68 subjects which were
captured with 13 different poses, 43 different illumination conditions and 4 different facial
expressions, for total 41,368 colour images with resolution of 640x486. MIT face database
contains face images of 16 people having variations in pose, light, scale(zoom) including 6 levels
of Gaussian pyramid [14]. ORL database contains face images of 40 people along with the
variation of poses, expression snapshot time, background, occlusion, open eyes, close eyes and
glass etc. [14]. PF01 database contains the true color face image of 103 people (53 males and 50
females) representing 17 variations (1 normal face, 4 illumination variation, 8 pose variation, 4
expression variations) per person [3].
The available literatures bear the witness that a large number of researchers have expended very
much attention, efforts and time to develop the face image database for FRT. These efforts have
led to the construction of large database with the wide variety of different faces. It is not out of
place to mention here that to sweep out the deficiency of tampered face database, we have spent
our contribution and prepared the database of both real and tampered face images of same subject.
This contribution is divided in six sections. Section 2 explains the database description with
acquisition protocols and database profile and section 3 demonstrates issues and challenges for
database acquisition. Section 4 demonstrates the database pre-processing for evaluation while
section 5 contains performance of face recognition algorithms on tampered face images. Last
section 6 includes conclusion and future scope.
2. DATABASE DESCRIPTIONS
The collection of a large number of heterogeneous objects in any domain is very challenging in
all respect. Unlike face recognition, no standard benchmark database is available in public
domain for tampering detection. Therefore, we have made our own protocol and prepared the
database for vitality detection.

Computer Science & Information Technology (CS & IT) 15
Fig. 1: The sample of cheating image (Adopted from [26])
2.1. Database Acquisition Protocol
For the effectiveness of database we have prepared four types of heterogeneous database using
coloured 12.2 megapixels, 5x optical stabilized camera. The images have been taken at a distance
of nearly 24 cm. to 30 cm. in an uncontrolled environment. The captured images are natural
images without imposing any constraints neither on the targeted subject nor their surrounding
such as background and illumination etc. More than 12 months of time have been spent for the
database preparation. Samples of obtained face images are shown in fig. 2.
Fig. 2: Sample face images
For efficient and reliable database acquisition we have set our protocol and acquired the said
database. We have taken the photographs of 10 pose (3 right pose, 4 left pose, 1 frontal pose, 1
pose 100
upward from front and 1 pose 100
downward from front) of each subjects. The camera
position is set at the approximated angles shown in the Fig.3 and obtained sample images are
shown in Fig.4. Angles between the poses are maintained by θ = x
r⁄ radians, where x is the 'arc'
size and r is approximated distance of camera from the targeted subject [25]. We have taken the
images in natural outdoor environment where neither camera nor targeted subjects are set at
accurately fixed position. For database acquisition we have set a protocol to take the tampered
face image on the same background and lighting effect as on real face imaging of same subjects.
2.2 Database Profile
For our assertion, we have prepared two types of database: Real face image database and
tampered face image database.
Fig. 3: Camera positions for the pose variation (adopted from [25])

Fig. 4: Pose variation of captured face images (adopted from [25])
2.2.1 Real Face Image Database - For real face image database we have acquired two databases.
i) From Standard organizations – We have collected 100 face images from standard publically
available database organizations.
From PIE Database – Collected the face images of 30 subjects with 10 poses per subjects of
equal lighting conditions.
From AR Database – Collected the face images of 30 subjects with 10 poses per subjects of equal
lighting conditions.
From Yale B database – Collected the face images of 40 subjects with 10 poses per subject of
equal lighting conditions. Sample face images of standard organizations are shown in Fig. 5.
Fig. 5: Samples of benchmark face images of standard organization
ii) Own prepared real face image database – We have acquired the real face image database of
150 volunteers and captured 10 poses of each volunteers from the camera positions as said earlier.
Sample real face images of own prepared database are shown in Fig. 6.

Fig. 6: Samples of own acquired face images
2.2.2 Tampered Face Image Database – In this section, we have categorized the database,
imposed with three types of tampering and acquired the images.
i) Dummy Face Image - For 100% tampered face we have acquired 200 dummy face images
which are bifurcated as 120 females and 80 males. Dummies are available at various public
places in uncontrolled environment and in unconstrained condition. Acquired dummy face images
are natural day light images shown in Fig. 7.
Fig. 7: Sample Dummy Face Images
ii) Colour Imposed Face Image - Colour imposed face images of volunteers described above are
acquired by applying synthetic colour on facial surface. 60 volunteers were not convinced to
tamper their faces. Hence only 90 subject’s colour imposed face images are acquired for database
at said protocol. In this category, database of each subject with nearly 100%, 60% and 30%
tampering of face surface are acquired. The sample of colour imposed face images are shown on
Fig. 8.
(a) (b) ( c)
Fig. 8: Colour Imposed Face Images
(a) Nearly 100%, (b) Nearly 60% and (c) Nearly 30% tampered
iii) Masked Face Image - Only 120 volunteers (out of 150) were convinced for masked face
photo session. For masked face preparation, a cosmetic cream is used whose effect looks
equivalent to the mask when imposed on the facial skin. In this category, database of each subject
with nearly 100%, 60% and 30% tampering of face surface are acquired. The sample masked
face images are shown on Fig.9.

(a) (b) (c)
(b)
Fig. 9: Masked Imposed Face Images
(a) Nearly 100% tampered, (b) Nearly 60% and (c) Nearly 30% tampered
3. ISSUES AND CHALLENGES IN DATABASE ACQUISITION
There are so many challenges to develop a comprehensive and adequate face image database.
One of the most fundamental problems is ability to take consistent, high-quality, repeatable
images. To produce repeatable results a lot of fundamental variables, such as image background,
illumination, snapshot time etc. must be controlled. While the most obvious variables involved
are the lighting either by camera equipment (flash) or by the environment (day, night, cloud, fog
etc.). To take the stable and consistent images electromechanical equipments are required. A lot
of issues and challenges are involved in our face image database acquisition and some of them are
mentioned as follows-
• In real life scenario, it is not easy to acquire each and every type of desired photographs
in laboratory. We have to go various places for required imaging. It is very time
consuming to move from one place to another to take the photographs.
• The preparation of database of one volunteer takes nearly 50 minutes (10 minutes for non
tampered face imaging, 20 minutes for colour imposed face imaging, 20 minutes for
masked face imaging).
• Most of the time, the volunteers get monotonous and feel irritation from the photo
session.
• For the acquisition of own database, the selection of volunteers depending upon their
availability of time for the photo session is very problematic.
• Most of the time volunteers were not convinced to spend 50 minutes of time for image
acquisition.
• 60 volunteers were not convinced to tamper their face from synthetic colours and 30 were
not convinced to tamper their face for mask preparation.
• Dummy faces are not available at single places which are spread out at various public
places. That’s why; it is time consuming to move from one place to other place for
photograph acquisition.
• It is very difficult to convince the owner of the dummies to capture the face image of
dummies. Most of the time they require the advertisement of their business and
showrooms.
• Unlike real faces we don’t have any control over pose, expression, illumination and
occlusion on dummy faces. So without any artificiality we have taken the photographs
which are available in the public places or markets.
• For the adequate database, the face and camera both should be still but in our case camera
stand could not be setup at the different-different public places.
• Since, the database contains outdoor face images situated at public places. Therefore, the
background of images could not be controlled.
• Weather is always not in the favorable condition for the image acquisition.

4. DATABASE PRE-PROCESSING
The obtained images are coloured images with the variety of lighting and shadowing effects.
Processing of any algorithm on the coloured image will take a lot of time. Therefore, for the
testing of various algorithms we require pre-processing. We have done the pre-processing steps as
shown in Fig.10.
4.1 Rotation
The photographs are taken in natural outdoor environment without any constraints and without
any stable camera setting. Hence, some time eyes of the subjects are not in horizontal position. To
setup the eyes in horizontal position, rotation (in the same plane of image) of image is required.
The sample of rotation is shown in Fig.10.
4.2 Cropping
A lot of background effects are available in the obtained images. To remove the huge background
effect, we have cropped the face from huge background scenes. The sample of cropping is shown
in Fig. 10.
4.3 Illumination Compensation
Finally, all tampered face images have normalized to set all the subjects at normal gray level
illumination and of same size [21].
Original Rotated Cropped Fig. 11: Color to Gray scale
Fig. 10: Pre-processed Images
Illumination covariate together with pose is a real challenge in face recognition. Gross et. al. [28]
describes that illumination, together with pose variation, is the most significant factor that alters
the appearance of faces. The images of our database are captured during day time in outdoor
environment, but are affected by change in weather conditions. Moreover, extreme lighting
produce shadow and too bright images, which may diminishes certain facial features and affect
the automatic recognition process [22].
In last decade Face Modeling, Normalization and Preprocessing, and Invariant Features
Extraction approaches have been addressed to resolve the illumination problem up to the certain
level [23]. In our case, we have used normalization and preprocessing approach for illumination
compensation because the algorithm, of this category doesn’t require any training and modeling
steps [25] and found satisfactory normalised images as an example shown in Fig. 11.
When illumination in gray scale image is high, normalization process reduces the illumination
and when illumination in gray scale image is low, normalization process improves the
illumination.

5. PERFORMANCES OF FACE RECOGNITION ALGORITHMS ON
TAMPERED FACES
5.1 Evaluation Algorithms
To evaluate the performance of our developed tampered face database on face recognition
algorithms we have selected four well-known holistic feature based classical algorithms : PCA,
ICA, LDA and SVM.
5.1.1 Principal Component Analysis (PCA) - PCA commonly uses the eigenfaces in which the
probe and gallery images must be the same size as well as normalized to line up the eyes and
mouth of the subjects whining the images [4],[6]. Approach is then used to reduce the dimension
of data by the means of image compression basics [7] and provides most effective low
dimensional structure of facial pattern. This reduction drops the unuseful information and
decomposes the face structure into orthogonal (uncorrelated) components known as eigenfaces.
Each face image is represented as weighted sum feature vector of eigenfaces which are stored in
1-D array. A probe image is compared against the gallery image by measuring the distance
between their respective feature vectors then matching result has been disclosed. The main
advantage of this technique is that it can reduce the data needed to identify the individual to
1/1000th
of the data presented [8].
In Eigenspace terminology, each face image is projected by the top significant eigenvectors to
obtain weights which are the best linearly weight the eigenfaces into a representation of the
original image. Knowing the weights of the training images and a new test face image, a nearest
neighbour approach determines the identity of the face.
5.1.2 Independent Component Analysis (ICA) - Independent Component Analysis [9] can be
viewed as a generalization of PCA [5]. While PCA de-correlates the input data using second-
order statistics and thereby generates compressed data with minimum mean-squared re-projection
error, ICA minimizes both second-order and higher-order dependencies in the input. It is
intimately related to the blind source separation (BSS) problem, where the goal is to decompose
an observed component into a linear combination of unknown independent components [20, 22].
And then recognition is performed.
5.1.3 Linear Discriminant Analysis (LDA) - Linear Discriminant Analysis is a statistical
approach for classifying samples of unknown classes based on training samples with known
classes. This technique aims to maximum between-class (across users) variance and minimum
within class (within user) variance. In these techniques a block represents a class, and there are a
large variations between blocks but little variations within classes.
5.1.4 Support Vector Machine (SVM) - Support Vector Machine (SVM) is very popular binary
classifier as methods for learning from examples in science and engineering. The performance of
SVM is based on the structure of the Riemannian geometry induced by the kernel function.
Although, SVM is binary classifier but now-a-days it received much attention for their
applicability in solving pattern recognition problems. It computes the support vectors by the
determination of hyper-plane that maximises the margin between the hyper-plane or closest
points [27].
5.2 Experimental Evaluation
For our evaluation process we have selected 6 pre-processed non-tampered face images of each
subject as training dataset and 4 (2 colour imposed and 2 masked) pre-processed tampered face as

test dataset. In the case of dummy face images, training and testing both images are dummy face
images and considered in the category of non-tampered face images because real face image of
those dummies are not available. The size of original images are 250x300 pixels denoted by L଴.
All images are compressed with the help of Gaussian kernel [24] to obtain higher level of
compressed images as Lଵ, Lଶ and Lଷ. Where Lଵ are of size 125x150, Lଶ are of size 63x75 and Lଷ
are of size 32x38 pixels.
5.2.1 Experimental Results - We have done our experiments on above four algorithms and
obtained results are shown in Table 1.
Fig. 12: Graph of Identification Accuracy of Real non-tampered Faces
Table 1: Identification Accuracy for various area of surface of face tampering
Training/Testing
60/40 %
Gaussian Compression
Levels
PCA ICA LDA SVM
Non-tampered
/ Non-Tampered
‫ܮ‬଴ 92.3 89.4 94.2 95.1
‫ܮ‬ଵ 92.1 91.6 93.9 94.2
‫ܮ‬ଶ 87.8 84.6 89.2 89.8
‫ܮ‬ଷ 82.6 82.9 83.2 84.0
Non-tampered
/ 30 % tampered
‫ܮ‬଴ 89.1 87.2 88.3 94.0
‫ܮ‬ଵ 88.7 86.8 88.1 93.6
‫ܮ‬ଶ 85.5 82.6 83.8 89.1
‫ܮ‬ଷ 82.5 80.6 79.8 82.9
Non-tampered
/ 60 % tampered
‫ܮ‬଴ 83.9 83.3 85.7 89.4
‫ܮ‬ଵ 83.6 80.2 85.2 88.8
‫ܮ‬ଶ 80.8 79.1 81.4 84.1
‫ܮ‬ଷ 78.5 78.8 78.7 80.3
Non-tampered
/ ~ 100 %
tampered
‫ܮ‬଴ 81.4 82.8 83.5 86.2
‫ܮ‬ଵ 81.0 79.7 82.9 85.9
‫ܮ‬ଶ 76.7 75.1 76.9 81.8
‫ܮ‬ଷ 75.8 72.6 76.0 79.5
5.2.2 Experimental Analysis –The results show that the identification accuracy varies
significantly depending upon the size of image, tampering area of the facial surface,
environmental constraints and algorithms. The reason behind these variations are described as –
• Fig. 12, 13, 14, and 15 demonstrate that identification accuracy of all mentioned
algorithms decrease as we increase the Gaussian level of compression.
• From Fig. 12 shows that highest identification accuracy at every level of Gaussian
compression, it demonstrate that when face surfaces are not tampered the accuracy will
be higher than tampered face.

Fig. 13: Graph of Identification Accuracy of 30 % Tampered Faces
Fig. 14: Graph of Identification Accuracy of 60 % Tampered Faces
• It is clearly visible from Fig. 12, 13, 14, and 15 that the performance of all mentioned
algorithms decreases on increasing the tampering area of facial surface.
• In the case of tampered face the graphs shown from Fig. 12, 13, 14, and 15 demonstrate that
the identification accuracy of used algorithms is unpredictable at higher level of
compression.
• From the above results it is unpredictable that which algorithm will be well suited for the
tampered face recognition.
• The above results also demonstrate that on every level of compressed image it is not
possible to select any particular algorithm.
• On compressing the images there is loss of some of their important features and therefore at
higher level of compression, accuracy decreases in all case of algorithms and tampering.

Fig. 15: Graph of Identification Accuracy of nearly 100 % Tampered Faces
• From each Fig. 12, 13, 14, and 15 it is clear that the performance of identification of SVM is
high in every case.
6. CONCLUSION AND FUTURE SCOPE
Generally face recognition algorithms are developed based upon their facial properties. Therefore,
in this paper we have selected holistic feature based algorithms to evaluate the identification
accuracy and done number of experiments for tampered face. To select the category of algorithm
for tampering but the results of our experimental are very fluctuating in all cases of compression.
Therefore, it is totally unpredictable to select any particular type of algorithm for tampered face
recognition. According to our hypothesis there should be separate module for the face tampering
detection and integrated to the face recognition system.
We have evaluated the identification accuracy of tampered face concluded with possible research
direction that i) Size of database should be increased to select the particular algorithm for
tampering detection. ii) Some new algorithms are to be developed to detect the tampering in real
world scenarios.
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