50120140504002

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 11-23 © IAEME
11
A NEW FACE RECOGNITION SCHEME FOR FACES WITH EXPRESSIONS,
GLASSES AND ROTATION
Walaa M Abdel-Hafiez1
, Mohamed Heshmat2
, Moheb Girgis3
, Seham Elaw4
1, 2, 4
Faculty of Science, Mathematical and Computer Science Department,
Sohag University, 82524, Sohag, Egypt
3
Faculty of Science, Department of Computer Science,
Minia University, El-Minia, Egypt
ABSTRACT
Face recognition is considered as one of the hottest research areas in computer vision field.
The purpose of the proposed research work is to develop an algorithm that can recognize a person by
comparing the characteristics of his/her face, which may have expressions, glasses and/or rotation, to
those of known faces in a database. This work provides a simple and efficient technique to recognize
human faces. The new method is based on variance estimation of the three components of the color
faces images and facial features extraction of the most facial features. The features under
consideration are eyes, nose and mouth. The technique used to extract facial features was developed
based on feature location with respect to face dimensions. The proposed algorithm has been tested on
various face images and its performance was found to be good in most cases. Experimental results
show that our method of human face recognition achieves very encouraging results with good
accuracy, great speed and simple computations.
Keywords: Face Recognition, Facial Features Extraction, Color Spaces, Variance Estimation.
I. INTRODUCTION
Face recognition has been used in various applications where personal identification is
required like, visual attendance systems where student identification and recognition are achieved
through face recognition. Face recognition [1-4] has been used also in gaming applications, security
systems, credit-card verification, criminal identifications, and teleconference, and short face
recognition applications are widely used in many corporate and educational institutions.
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 5, Issue 4, April (2014), pp. 11-23
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2014): 8.5328 (Calculated by GISI)
www.jifactor.com
IJCET
© I A E M E

12
Human faces are complex objects with features that can vary over time. However, humans
have a natural ability to recognize faces and identify persons with just a glance. Our natural
recognition ability also extends beyond face recognition, where we are equally able to quickly
recognizing patterns, sounds or smells. Unfortunately, this ability does not exist in machines, thus the
need to simulate recognition artificially in our attempts to create intelligent autonomous machines.
Facial feature recognition is an example of popular applications for artificial intelligence systems.
Face recognition by machines has various important applications in real life, such as, electronic and
physical access control, national defense and international security. Simulating our face recognition
natural ability in machines is difficult but not impossible. Throughout our lifetime, many faces are
seen and stored naturally in our memories which forming a kind of database. Machine recognition of
faces requires also a database, which consists of facial images that may include different face images
of the same person. The development of intelligent face recognition systems requires providing
sufficient information and meaningful data during machine learning of a face.
Face Recognition can be defined as the visual perception of familiar faces or the biometric
identification by scanning a person's face and matching it against a database of known faces. In both
definitions, the faces can be identified as familiar or known faces.
One of the main challenging problems in building an automated system that perform face
recognition and verification tasks is face detection and facial feature extraction. Though people are
good at face identification, recognizing the human faces automatically by computer is a very difficult
task. Face recognition is influenced by many complications, such as the differences of facial
expression, the light directions of imaging, and the variety of posture, size and angle. Even for the
same person, the images taken in a different surrounding condition may be unlike. The problem is so
complicated that the achievement in the field of automatic face recognition by computer is not as
satisfied as the finger prints [5].
The objective of facial feature localization is to detect the presence and location of features
after the locations of faces are extracted by using any face detection method. The challenges
associated with face and facial feature detection methods can be attributed to the following
factors [6]:
• Intensity: There are three types of intensity: color, gray, and binary.
• Pose: Face images vary due to the relative camera-face pose (frontal, 45º, profile), and some
facial features such as an eye may become partially or wholly occluded.
• Structural components: Facial features such as beards, mustaches, and glasses may or may not
be present.
• Image rotation: Face images directly vary for different rotations.
• Poor quality: Image intensity in poor-quality images, for instance, blurry images, distorted
images, and images with noise, becomes unusual.
• Facial expression: The appearance of faces depends on a personal facial expression.
• Unnatural intensity: Cartoon faces and rendered faces from 3D model have unnatural intensity.
• Occlusion: Faces may be partially occluded by other objects such as hand, scarf, etc.
• Illumination: Face images vary due to the position of light source [6].
Phimoltares et al. [6] presented algorithms for all types of face images in the presence of
several image conditions. There are two main stages in their method. In the first stage, the faces are
detected from an original image by using Canny edge detection and their proposed average face
templates. Second, a proposed neural visual model (NVM) is used to recognize all possibilities of
facial feature positions. Input parameters are obtained from the positions of facial features and the
face characteristics that are low sensitive to intensity change. Finally, to improve the results, image
dilation is applied for removing some irrelevant regions.

13
Nikolaidis and Pitas [7] proposed a combined approach for facial feature extraction and
determination of gaze at direction that employs some improved variations of the adaptive hough
transforms for curve detection, minima analysis of feature candidates, template matching for inner
facial feature localization, active contour models for inner face contour detection and projective
geometry properties for accurate pose determination.
Koo and Song [8] suggested defining 20 facial features. Their method detects the facial
candidate regions by haar classifier, and detects eye candidate region and extracts eye features by
dilate operation, then detect lip candidate region using the features. The relative color difference of
a* in the L*a*b* color space was used to extract lip feature and to detect nose candidate region and
detected 20 features from 2D image by analyzing end of nose.
Yen and Nithianandan [9] presented an automatic facial feature extraction method based on
the edge density distribution of the image. In the preprocessing stage, a face is approximated to an
ellipse, and a genetic algorithm is applied to search for the best ellipse region match. In the feature
extraction stage, a genetic algorithm is applied to extract the facial features, such as the eyes, nose
and mouth, in the predefined sub regions.
Gu et al. [5] proposed a method to extract the feature points from faces automatically. It
provided a feasible way to locate the positions of the two eyeballs, near and far corners of eyes,
midpoint of nostrils and mouth corners from face image.
Srivastava [10] proposed an efficient algorithm for facial expression recognition system,
which performs facial expression analysis in a near real time from a live web cam feed. The system is
composed of two different entities: trainer and evaluator. Each frame of video feed is passed through
a series of steps, including Haar classifiers, skin detection, feature extraction, feature point tracking,
creating a learned support vector machine model to classify emotions to achieve a tradeoff between
accuracy and result rate.
Radha and Nallammal [11] described a comparative analysis of face recognition methods:
principle component analysis (PCA), linear discriminant analysis (LDA) and independent component
analysis (ICA) based on curvelet transform. The algorithms are tested on ORL Database.
Kumar et al. [12] presented an automated system for human face recognition in a real time
background world for a large homemade dataset of persons' faces. To detect real time human face
AdaBoost with Haar cascade is used and a simple fast PCA and LDA are used to recognize the faces
detected. The matched face is then used to mark attendance in the laboratory, in their case.
El-Bashir [13] introduced a method for face recognition. After a preprocessing and
normalization stage to the image, PCA is applied to recognize a specified face. If the face is not
recognized correctly, then more features are extracted: face color and moment invariant. The face is
recognized again using decision tree.
Javed [14] proposed a computer system that can recognize a person by comparing the
characteristics of face to those of known individuals. He focused on frontal two-dimensional images
that have been taken in a controlled environment, i.e. the illumination and the background were
constant, and used the PCA technique. The system gives good results especially with angled face
views.
Pattanasethanon and Savithi [15] presented a novel technique for facial recognition through
the implementation of successes mean quantization transform and spare network of winnow with the
assistance of eigenface computation. After having limited the frame of the input image or images
from web-cam, the image has cropped into an oval or ellipse shape. Then the image is transformed
into grey scale color and is normalized in order to reduce color complexities. They also focused on
the special characteristics of human facial aspects such as nostril areas and oral areas, compared the
images obtained by web-cam with images in database, and have good accuracy with low time.

14
Wu et al. [16] presented a system that can automatically remove eyeglasses from an input
face image. The system consists of three modules: eyeglasses recognition, localization and removal.
Given a face image, first, an eyeglasses classifier is used to determine if a pair of eyeglasses is
present. Then, a Morkov chain Monte Carlo method is applied to locate the glasses by searching for
the global optimum of the posteriori. Finally, a novel example based approach has been developed to
synthesize an image with eyeglasses removed from the detected and localized face image. The
experiments demonstrated that their approach produces good quality of face images with eyeglasses
removed.
Chen and Gao [17] presented a local attributed string matching (LAStrM) approach to
recognize face profiles in the presence of interferences. The conventional profile recognition
algorithms heavily depend on the accuracy of the facial area cropping. However, in realistic scenarios
the facial area may be difficult to localize due to interferences (e.g., glasses, hairstyles). The proposed
approach is able to efficiently find the most discriminative local parts between face profiles
addressing the recognition problem with interferences. Experimental results have shown that the
proposed matching scheme is robust to interferences compared against several primary approaches
using two profile image databases (Bern and FERET).
This paper presents a new face recognition method for faces with expressions, glasses and/or
rotation. The proposed method uses variance estimation of RGB components to compare the
extracted faces and the faces in the database used in comparison. In addition, Euclidean distance of
facial features of the extracted faces from test image and faces extracted from the database after a
variance test is used.
The rest of this paper is organized as follows. Section II describes the methodology of the
proposed method with its stages: variance estimation, feature extraction, method representation and
the proposed algorithm. Section III presents the results and method analysis. Section IV draws the
conclusion of this work and possible points for future work.
II. METHODOLOGY
The face and facial feature detection algorithms are applied to detect generic faces from
several face images. Most automatic face recognition approaches are based on frontal images. Facial
proﬁles, on the other hand, provide complementary information of the face that is not present in
frontal faces. Fusion of frontal and proﬁle views makes the overall personal identification technique
foolproof and efficient.
The proposed face recognition method is based on the average variance estimation of the
three components of RGB faces images, and the extraction of the most facial features. The features
under consideration are eyes, nose and mouth. The technique used to extract facial features is based
on feature location with respect to the dimensions of the face image.
Given a face image, which obtained from a camera or preprocessed previously, our goal is to
identify this face image using a database of known humans' faces. Therefore, our algorithm is divided
into three main steps. First: variance estimation of faces images. Second: facial feature extraction,
an effective method to extract facial features like eyes, nose and mouth depending on their locations
with respect to the face region is used, which we have developed before in [18]. Third: similar face
identification or image searching; the goal of this step is to scan the database of known faces to find
the most similar faces to the test face.

15
1. Variance Estimation
Variance calculation is a very light calculation and considered as an important constraint to
prove similarity between two images. Let x be a vector of dimension n, the variance of x can be
calculated as follows:
n
xix
n
i
∑ −
= =1
)(
var
2
, (1)
where x is the mean value of x .
However, it is not necessary that the two images which have the same variance to have the
same contents. Different images may have the same value of variance because variance estimation is
totally depending on the values of image pixels and their mean value. So the variance is used at first
to filter the database of faces and extract faces that have the same or close value to variance of the
input face image, then another test is required to detect the most similar faces to this test face [18].
When working with RGB color images, there are three values for each pixel in the image,
representing the red, green, and blue components. To compute the variance of RGB image, the
variance for each color is calculated separately. So there are three values for variance, one for the red
values, another for the green values and third for the blue values [18], which are calculated as
follows:
2 2 2
1 1 1
( ) ( ) ( )
, , ,
n n n
r r g g b b
i i i
red green blue
x x x x x x
v v v
n n n
= = =
− − −
= = =
∑ ∑ ∑
(2)
To simplify the comparison, the average of the three values is computed as follows:
( )
,
3
red green bluev v v
v
+ +
= (3)
2. Facial Features Extraction
In this part of work, the aim is to compare two color faces to decide whether they both belong
to the same person or not and detect the similarity between them using Euclidean distance. RGB
(Red, Green and Blue) color space, fig. 1, which is used here, is an additive color system based on
tri-chromatic theory. It is often found in systems that use a CRT to display images. The RGB color
system is very common, and is being used in virtually every computer system as well as television,
video etc [19], [20].
Red Green Blue
Figure 1: RGB color model

16
In RGB color model, any source color (F) can be matched by a linear combination of three
color primaries, i.e. Red, Green and Blue, provided that none of those three can be matched by a
combination of the other two, see fig. 1.
Here, F can be represented as:
,F r R gG bB= + + (4)
where r, g and b are scalars indicating how much of each of the three primaries (R, G and B) are
contained in F. The normalized form of F can be as follows:
' ' ' ,F R R G G B B= + + (5)
where
' / ( ) ,
' / ( ) , (6)
' / ( ) ,
R r r g b
G g r g b
B b r g b
= + +
= + +
= + +
To extract facial features, we used our method proposed in [18], which is based on feature
location with respect to the whole face region. By detecting the candidate regions of left eye, right
eye, nose and mouth, by training, then applying the obtained dimensions of each region on several
other faces with the same size, the results were very good, as shown in fig. 2.
Given a face image of 200 pixels height and 200 pixel width, after training with a lot of
images, we found that the candidate region of eyes is located between rows 60 and 95, columns 25
and 80 for right eye and columns 115 and 170 for left eye. The candidate region for the nose is
located between rows 110 and 145 and columns 75 and 125 and the candidate region for the mouth is
located between rows 145 and 185 and columns 60 and 135. When applying the dimensions obtained
by training on many face images, we found that they there were suitable for any face image with the
same width and height even it has expression, as shown in fig. 2.
Figure 2: Examples of Feature extraction
(6)

17
This feature extraction technique can be generalized and the candidate region for each feature,
which is based on height and width of the face image to match any face image size can be as follows:
• Right eye: Rows from (height/3.3) to (height /2.1)
Columns from (width/8) to (width/2.5)
• Left eye: Rows from (height/3.3) to (height /2.1)
Columns from (width/1.7) to (width/1.17)
• Nose: Rows from (height/1.8) to (height /1.38)
• Mouth: Rows from (height/1.38) to (height /1.08)
3. Method Representation
The proposed algorithm consists of three parts. Firstly, variance estimation is applied to
extract database images, which have a close variance value to the test image. Secondly, the features
extraction method is used to extract facial features from the face images. Finally, Euclidean distance
of facial features is computed by the following equation:
[ ] [ ]( )
[ ] [ ]( )
[ ] [ ]( )
d abs test feature R matched feature R
abs test feature G matched feature G
abs test feature B matched feature B
= − +
− +
−
(7)
By applying eq. (7) to find the distance between the right eye region of the test image and the
right eye region of each image, which has variance value close to the variance value of the test image
(returned from variance test), then, by applying eq. (7) to left eye, nose and mouth regions and find
summation of these four distance values, it can be decided which of the images that have close
variance value is the most similar to test image. The steps of the proposed algorithm are shown in
fig. 3.
Step 1: Read input image.
Step 2: Read the database of images and calculate the variance of each image by using eq. (2) , (3), and
put variance values in an array,
Step 3: Calculate variance of the test image using eq. (2), (3).
Step 4: Compare variance value of test image and each image in database and keep locations of the most
similar images to test image, which satisfy the condition ( )600 variance difference 600− ≤ ≤ , in an array.
Step 5: For i=1 to number of similar images which extracted from step 4.
a) Extract facial features from each image according to location (right eye – left eye – nose –
mouth).
b) Calculate the Euclidean distance between the 3-arrays containing the RGB color values of each
feature using eq. (7) plus the Euclidean distance between the 3-arrays containing the RGB color
values of the whole test image and each similar image from step 4.
Step 6: Detect minimum distance (d ) and location of the image that has the minimum distance from step
5.
Step 7: Display the best-matched image from the database.
Figure 3: The proposed algorithm's steps

18
III. RESULTS AND DISCUSSION
The experiments were performed on a computer with 2.20 GHz speed and 4 Gbyte RAM
using several color images containing faces and a database of 10 different images with different
sizes, as shown in fig. 4. The test images include different images for the same person with different
conditions. Some images have expressions, glasses and some rotation, as shown in fig. 5. The
images in the database have been chosen carefully such that they are standard and have no
expressions if possible.
The proposed algorithm gives good results in recognizing all the test images, which belong to
the same person in the database, with different expressions, glasses and rotation. Even if the gaze
direction is different, the proposed algorithm succeeds in returning the correct location of the right
image in the used database.
Figure 4: The used database
Figure 5: The used database and some of test images
Table 1 shows some of the results obtained using 150 test RGB images of 10 different
persons and a database of 10 standard RGB images of those persons, which is shown in fig. 4. The
first column of the table shows the test face. The next columns show the results that were obtained by
applying the classical method, variance estimation formula, feature extraction method, the proposed
method and the time in each method. The classical method is the general method in comparing two
images, by comparing pixel by pixel and computing the summation of the difference of all pixels.
The classical method performs on the whole image without partitioning. The variance estimation is
54321
109876

19
applied by using eq. (2) and (3). We have displayed the results of the variance separately to show
how the variance computation is efficient and important in comparing similarity between images.
When we used the variance estimation as a first test in the proposed method, we noticed that it gives
the correct image location from the database if the test image and the matched image in the database
have similar conditions of illumination and background. Also, we have applied the feature extraction
method separately to study how it is efficient in face recognition. Facial features are extracted and
Euclidean distance is computed for each feature then the summation of the difference is obtained. By
comparing the difference between the test image and all the images in the database, the matched
image is detected as the image has minimum difference. It is noticed from the table that the classical
method and variance estimation method have less time than the two other methods. The execution of
the proposed method proceeds as follows: the first test (variance test) with variance difference range
equals [-600,600] is applied first to detect the images that have close variance values to the test
image. (It should be noted that the variance difference range is arbitrary and can be changed). The
algorithm returns the locations of faces whose variance value close to the variance of the test face. In
order to know which one of them is the same or the closest to the test face, the facial features of the
test face and the facial features of the obtained face images are extracted then the Euclidean distance
of their RGB components is calculated by eq. (7). The face image with the minimum distance (d) is
considered as the best-matched image and its location is returned.
The search efficiency is evaluated by how many times the distance (d) computations are
performed on average compared to the size of the database. In the proposed method the total number
of distance calculations is small, because it uses the variance test to find out the face images that have
a close variance value to the input face image, then the distance computation is performed only on
those images where their number is always small compared to the database size. But execution time
of the proposed method is high compared to the other methods because the proposed method works
in two stages or two tests variance estimation and facial feature extraction where each of these stages
take some time. The execution time depends on the database size. The execution time of the classical
method is 0.43 seconds on average, the execution time of the variance estimation method is 0.1
seconds on average, the execution time of facial feature extraction method is 0.22 seconds on average
and the execution time of the proposed method is 1.06 seconds on average, as shown in Fig. 6.
In most cases, the proposed algorithm gives good results. However, in some cases, the results
are not good, because the proposed algorithm is affected by illumination conditions in some images,
zooming and big rotation in some others, see Table 2.
Figure 6: The time chart of the proposed algorithm and comparison methods
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Time(inseconds)
Images

20
Table 1: Some results of the proposed algorithm and the comparison methods

21
Table 1: Some results of the proposed algorithm and the comparison methods (Continued)

22
Table 2: Some false positive results of the proposed algorithm and the comparison methods
IV. CONCLUSION
In this paper, a new method of face recognition, for faces with expressions, glasses and/or
rotation, based on variance estimation and facial feature extraction is proposed. It can be used in face
recognition systems such as video surveillance, human computer interfaces, image database
management and smart home applications. The proposed algorithm has been tested using a database
of faces and the results showed that it is able to recognize a variety of different faces in spite of
different expressions, rotation and illumination conditions. Zoomed images and their effect on the
recognition of humans need further investigation.
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