Independent Research

Project report on Face Detection
And Recognition
By Naveen Kumar Kavvadi
(542052962)
Guided by
PROF DR IKHLAS ABDEL QADER
And
HAITHAM AL ANSARI
2015

ECE 6970 FACE DETECTION
Abstract
In this paper I researched about the best face detection techniques and the limitations with that.
For face detection the most common way methods are feature based, holistic, and hybrid
approach. In this research paper we proposed several methods for the detection and we also
designed an algorithm with the three methods together. For face detection technique we used
viola jones and Haar method. Shi and Thomasi algorithm is used to extract feature points in face
for detection.
Keywords: Face Detection, Viola Jones, Haar method
Introduction
Face recognition is a challenge in image analysis and computer vision and received a great
attention in last few years. Here we have mentioned some of the face recognition techniques
that are worth useful and there are many like these techniques. Basic format of face recognition
is we already have a data base for registered faces and an algorithm that is designed to verify the
input image with the data base and confirm the identities. Face recognition have become more
useful for the security reasons also because in previous everyone uses the pin, passwords, etc.
for identification but face recognition, vein recognition, iris, voice recognition and retina
recognition have become the more secured ways to deal with the security. Here we have
proposed many face detection techniques and based on that we found best ways to get the best
results
Diverse parts of human physiology zone unit won’t to validate a man's personality. The
exploration of determining the personality with reference to very surprising qualities
characteristic of human being is termed bioscience. The qualities trait may be by and large
grouped into 2 classes i.e. physiologicaland behavioral. Estimation of physical choices for private
distinguishing proof is partner age late take after that goes back to the Egyptians period. Be that
as it may, it was not till nineteenth century that the investigation of biosciencewas widely utilized
for private recognizable proof and security associated issues. With the headway in innovation,
recognizable proof has been wide utilized for access administration, implementation, and

security framework. somebody may be known on diverse physiological and behavioral attributes
like fingerprints, confronts, iris, hand immaculate science, walk, ear example, voice
acknowledgment, keystroke example and warm mark.
This paper proposes on color based, motion based, blink detection and feature detection
techniques that will all help. We have made a holistic, featured based and hybrid approach in
face detection techniques.
Survey on Face Detection:
Face acknowledgment is a test in picture investigation and PC vision and got an extraordinary
consideration in most recent couple of years. Here we have specified a portion of the face
acknowledgment systems that are worth helpful and there are numerous like these strategies.
Essential arrangement of face acknowledgment is we as of now have an information base for
enlisted faces and a calculation that is intended to check the info picture with the information
baseand affirmthe characters. Face acknowledgment have turned out to be more helpful for the
security reasons likewise on the grounds that in past everybody utilizes the pin, passwords, and
so on for ID yet confront acknowledgment, vein acknowledgment, iris, voice acknowledgment
and retina acknowledgment have turned into the more secured approaches to manage the
security.
Some of the surveys in face recognition for the best methods
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.446.3828&rep=rep1&type=pdf
http://mplab.ucsd.edu/~marni/Igert/Zhao_2003.pdf
http://www.face-rec.org/interesting-papers/general/zhao00face.pdf
http://ijcttjournal.org/Volume5/number-4/IJCTT-V5N4P136.pdf

Methods of Face detection and recognition:
Face discovery decides the vicinity and area of a face in a picture, by recognizing the face from
every single other example present in the scene. This requires suitable face displaying and
division. The methodology ought to likewise consider the wellsprings of variety of facial
appearance like review geometry (posture), enlightenment (shading, shadowing, and self-
shadowing), the imaging procedure (determination, center, imaging clamor, viewpoint impacts),
and different components like Occlusion. On the other hand, face recognition can be completed
by utilizing the whole face, making impediment hard to handle. Face identification procedures
characterized on the premise of the picture data used to help in location—shading, geometric
shape, or movement data, .The accompanying figure demonstrates the procedure of recognition
in a still picture or picture succession.
Face acknowledgment is such an indispensable piece of our lives and performed effortlessly that
we once in awhile stop to consider the intricacy of what is being finished.It is the essentialmeans
by which individuals recognize one another thus it is characteristic to endeavor to instruct PCs to
do the same. The uses of robotized face acknowledgment are various: from biometric validation
observation to video database indexing and looking.
Face acknowledgment frameworks are turning out to be progressively well known in biometric
verification as they are non-meddling and don't generally require the clients collaboration. On
the other hand, the acknowledgment precision is still not sufficiently high for expansive scale
applications and is about 20 times more regrettable than unique finger impression based
frameworks.

Image/video Face location, size or pose Aligned face Feature vector
Face ID
Fig above showing the face recognition in a flow chart
Face detection/
Tracking
Face
Alignment
Feature
Extraction
Feature
matching
Database of
enrolled
users
Still Image/Image
sequence
Data base of
faces
Segmentation of faces
Extraction of features
Tracking/ Identification

Fig showing the process of Face detection
Different types of face detection:
Face discovery is a PC innovation that decides the area and size of human face in self-assertive
(advanced) picture. The facial components are identified and some other articles like trees,
structures and bodies and so forth are overlooked from the computerized picture. It can be
viewed as a specific instance of article class discovery, where the assignment is discovering the
area and sizes of all articles in a picture that have a place with a given class. Face discovery, can
be viewed as a more general instance of face confinement. In face limitation, the undertaking is
to discover the areas and sizes of a known number of confronts (as a rule one). Essentially there
are two sorts of ways to deal with identify facial part in the given picture i.e. highlight base and
picture base approach. Feature base methodology tries to concentrate elements of the picture
and match it against the information of the face highlights. While picture base methodology tries
to get best match in the middle of preparing and testing pictures
1) Active Shape Model:

Dynamic shape models concentrate on complex non-inflexible components like genuine physical
and larger amount appearance of elements. Implies that Active Shape Models (ASMs) are gone
for naturally finding historic point focuses that characterize the state of any factually displayed
object in a picture. At the point when of facial elements, for example, the eyes, lips, nose, mouth
and eyebrows. The preparation phase of an ASM includes the building of a factual facial model
from a preparation set containing pictures with physically commented historic points. ASMs is
arranged into three gatherings i.e. snakes, PDM, Deformable layouts.
1.1) Snakes:
The principal sort utilizes a nonspecific dynamic shape called snakes, initially presented by Kass
et al. in 1987. Snakes are utilized to recognize head limits. With a specific end goal to accomplish
the undertaking, a snake is initially instated at the closeness around a head limit. It then bolts
onto close-by edges and along these lines accept the state of the head. The advancement of a
snake is accomplished by minimizing a vitality capacity, ESnake (relationship with physical
frameworks), meant as internal vitality is the part that relies on upon the inherent properties of
the snake and characterizes its characteristic development. The average normal development in
snakes is contracting or growing. The outer vitality neutralizes the inside vitality and empowers
the forms to go amiss from the common development and in the end expect the state of close-
by elements—the head limit at a condition of equilibria.
Esnake = E (internal) + E (External) Where Einternal and EExternal are internal and external
energy functions.
1.2) Deformable Templates:
Deformable layouts were then acquainted by Yuille et al with consider the earlier of facial
elements and to better the execution of snakes. Finding a facial component limit is not a simple
undertaking in light of the fact that the nearby proof of facial edges is hard to arrange into a
sensible worldwide substance utilizing nonspecific shapes. The low splendor contrast around
some of these elements likewise makes the edge recognition process tricky. Yuille et al. took the
idea of snakes above and beyond by consolidating worldwide data of the eye to enhance the

unwavering quality of the extraction process. Deformable layouts methodologies are produced
to take care of this issue. Disfigurement depends on neighborhood valley, edge, top, and shine.
Other than face limit, notable component (eyes, nose, mouth and eyebrows) extraction is an
extraordinary test of face acknowledgment.
E = Ev + Ee + Ep + Ei + E internal; where Ev, Ee, Ep, Ei, Einternal are external energy due to valley,
edges, peak and image brightness and internal energy
1.3) PDM (Point Distribution Model):
Freely of mechanized picture investigation, and before ASMs were produced, scientists created
factual models of shape [30]. The thought is that once you speak to shapes as vectors, you can
apply standard measurable systems to them simply like some other multivariate article. These
models learn suitablegroups of stars of shape focuses from preparing cases andutilize important
parts to manufacture what is known as a Point Distribution Model. These have been utilized as a
part of various routes, for instance for arranging Iron Age suggests. Perfect Point Distribution
Models can just disfigure in ways that are normal for the article. Cootes and his associates were
looking for models which do precisely that so if a whiskers, say, covers the button, the shape
model can override the picture to rough the position of the jaw under the facial hair. It was in
this way normal (yet maybe just by and large) to embrace Point Distribution Models. This
combination of thoughts from picture preparing and factual shape displaying prompted the
Active Shape Model. The first parametric measurable shape model for picture investigation in
view of main segments of bury point of interest separations was exhibited by Cootes and Taylor.
On this methodology, Cootes, Taylor, and their partners, then discharged a progression of papers
that aggregated in what we call the traditional Active Shape Model.
2) Low Level Analysis:
Based on low level visual features like color, intensity, edges, motion etc.
2.1) Skin color based:

Shading is an imperative component of human countenances. Utilizing skin-shading as an
element for following a face has a few points of interest. Shading preparing is much
speedier than handling other facial elements. Under certain lighting conditions, shading
is introduction invariant. This property makes movement estimation much simpler in light
of the fact that just an interpretation model is required for movement estimation.
Following human confronts utilizing shading as an element has a few issues like the
shading representation of aface acquired by a camera is impacted by numerous elements
(encompassing light, question development, and so on.)
Significantly three distinctive face discovery calculations are accessible in view of RGB,
YCbCr, and HIS shading space models. In the usage of the calculations there are three
fundamental steps viz.
(1) Classify the skin area in the shading space,
(2) Apply limit to cover the skin locale and
(3) Draw jumping box to extricate the face picture.
2.2) Motion based:
At the point when utilization of video arrangement is accessible, movement data can be
utilized to find moving items. Moving outlines like face and body parts can be separated
by essentially edge collected outline contrasts. Other than face districts, facial features
can be situated by casing contrasts.
2.3) Gray scale base:
Dim data inside of a face can likewise be regard as imperative components. Facial
elements, for example, eyebrows, students, and lips show up for the most part darker
than their encompassing facial areas. Different late element extraction calculations scan
for neighborhood dim minim inside fragmented facial districts. In these calculations, the
info pictures are initially upgraded by complexity extending and dim scale morphological
schedules to enhance the nature of nearby dull patches and in this way make recognition

less demanding. The extraction of dull patches is accomplished by low-level dark scale
edge. Based system and comprise three levels.
2.4) Edge based:
Face identification in light of edges was presented by Sakai et al. This work depended on
dissecting line drawings of the countenances from photos, expecting to find facial
elements. Than later Craw et al. proposed a various leveled system in view of Sakai et al
work to follow a human head plot. At that point after exceptional works were did by
numerous specialists in this particular range. Strategy recommended by Anila and
Devarajan exceptionally straightforward and quick. They proposed casing work which
comprise three stages i.e. at first the pictures are upgraded by applying middle channel
for clamor evacuation and histogram balance for differentiation modification. In the
second step the edge picture is built from the improved picture by applying sober
administrator. At that point a novel edge following calculation is connected to remove
the sub windows from the upgraded picture in view of edges. Further they utilized Back
engendering Neural Network (BPN) calculation to group the sub-window as either face or
non-face.
3) Feature Analysis:
These calculations expect to discover basic elements that exist notwithstanding when
the stance, perspective, or lighting conditions shift, and after that utilization these to
find faces. These systems are outlined basically for face limitation.
3.1.1) Viola Jones Method:
Paul Viola and Michael Jones introduced a methodology for item location which
minimizes calculation time while accomplishing high identification precision. Paul Viola
and Michael Jones proposed a quick and vigorous strategy for face identification which is
15 times faster than any method at the seasonof discharge with 95% exactness ataround
17 fps. The procedure depends on the utilization of basic Haar-like components that are
assessed rapidly through the utilization of another picture representation. In view of the

idea of an Integral Image it creates a substantial arrangement of elements and uses the
boosting calculation AdaBoost to decrease the over complete set and the presentation of
a degenerative tree of the supported classifiers accommodates hearty and quick
impedance. The locator is connected in a filtering mold and utilized on dark scalepictures,
the checked window that is connected can be scaled, and also the elements
3.1.2) Gabor Feature Method:
Sharif et al proposed an Elastic Bunch Graph Map (EBGM) calculation that effectively
executes face discovery utilizing Gabor channels. The proposed framework applies 40
distinctive Gabor channels on a picture. As a consequence of which 40 pictures with
distinctive edges and introduction are gotten. Next, most extreme power focuses in each
separated picture are computed and check them as guardian focuses. The framework
lessens these focuses in agreement to separation between them. The following step is
figuring the separations between the lessened focuses utilizing separation recipe. Finally,
the separations are contrasted and database. On the off chance that match happens, it
implies that the appearances in the picture are identified. Comparison of Gabor filter is
demonstrated as follows
Gives the frequency, gives the orientation.

3.2) Constellation Method:
All routines examined so far can track confronts yet at the same time some issue like finding
countenances of different postures in complex foundation is really troublesome. To diminish this
trouble examiner shape a gathering of facial elements in face-like star groupings utilizing more
vigorous demonstrating methodologies, for example, measurable investigation. Different sorts
of face star groupings have been proposed by Burl et al. They set up utilization of factual shape
hypothesis on the elements distinguished from a multi scale Gaussian subordinate channel.
Huang et al likewise apply a Gaussian channel for pre-preparing in a structure in view of picture
highlight investigation.
Image Base Approach:
1) Neutral Network:
Neural systems increasing substantially more consideration in numerous example
acknowledgment issues, for example, OCR, object acknowledgment, and self-sufficient
robot driving. Since face identification can be dealt with as a two class design
acknowledgment issue, different neural system calculations have been proposed. The
upside of utilizing neural systems for face discovery is the plausibility of preparing a
framework to catch the intricate class restrictive thickness of face examples. In any case,
one bad mark is that the system structural engineering must be broadly tuned (number
of layers, number of hubs, learning rates, and so forth.) to get remarkable execution. In
ahead of schedule days most progressive neural system was proposed by Agui et al. The
principal stage having two parallel sub systems in which the inputs are sifted force values
from a unique picture. The inputs to the second stage systemcomprise of the yields from
the sub systems and removed component values. A yield at the second stage
demonstrates the vicinity of a face in the info district. Propp and Samal created one of
the soonest neural systems for face location. Their system comprises off our layers with
1,024 information units, 256 units in the first shrouded layer, eight units in the second
concealed layer, and two yield units. Feraud and Bernier exhibited a discovery strategy
utilizing auto acquainted neural systems. The thought depends on which demonstrates

an auto cooperative system with five layers can perform a non-direct vital segment
investigation. One auto acquainted system is utilized to recognize frontal perspective
countenances and another is utilized to distinguish confronts swung up to 60 degrees to
one side and right of the frontal perspective. After that Lin et al. introduced a face
identification framework utilizing probabilistic choice based neural system (PDBNN). The
building design of PDBNN is like a spiral premise capacity (RBF) system with adjusted
learning standards and probabilistic elucidation
2) Linear Sub Space Method:
2.1) Eigen Faces Method:
An early case of utilizing Eigen vectors in face acknowledgment was finished by Kohen in
which a basic neural system is shown to perform face acknowledgment for adjusted and
standardized facepictures. Kirby and Sirovich recommended that pictures of appearances
can be straightly encoded utilizing an unassuming number of premise pictures. The
thought is ostensibly proposed first by Pearson in 1901 and afterward by Hoteling in
1933.Given an accumulation of n by m pixelpreparing pictures spoke to as avector of size
m X n, premise vectors traversing an ideal subspace are resolved such that the mean
square blunder between the projection of the preparation pictures onto this subspace
and the first pictures is minimized. They call the arrangement of ideal premise vectors
Eigen pictures subsequent to these are just the Eigen vectors of the co difference grid
figured from the vectored face pictures in the preparation set. Experiments with an
arrangement of 100 pictures demonstrate that a face picture of 91 X 50 pixels can be
adequately encoded utilizing just 50 Eigen pictures, while holding a sensible similarity
(i.e., capturing 95 percent of the fluctuation).

Examples of Eigen Faces
3) Statistical Approach:
3.1) Support Vector Machine:
SVMs were initially presented Osuna et al for face identification. SVMs fill in as another
worldview to prepare polynomial capacity, neural systems, or spiral premise capacity
(RBF) classifiers. SVMs takes a shot at prompting rule, called auxiliary danger
minimization, which focuses to minimize an upper bound on the normal speculation
mistake. A SVM classifier is a direct classifier where the isolating hyper plane is minimized
the normal characterization blunder of the concealed test patterns. In Osunaet al added
to a proficient strategy to prepare a SVM for extensive scale issues, and connected it to
face discovery. In light of two test sets of 10,000,000 test examples of 19 X 19 pixels, their
framework has marginally bring down blunder rates and runs roughly 30 times quicker
than the framework by Sung and Poggio. SVMs have likewise been utilized to identify
appearances and walkers in the wavelet area.
3.2) Principal Component Analysis:

PCA is a systemin light of the idea of Eigen confronts and was initially presented by Kirby
and Sirivich in 1988. PCA otherwise called Karhunen Loeve projection. Turk and Pentland
proposed PCA to face acknowledgment and identification. So also, PCA on a preparation
set of face pictures is performed to create the Eigen faces in face space. Pictures of
countenances are anticipated onto the subspace and grouped. So also, non-face
preparing pictures are anticipated onto the same subspace and grouped. To distinguish
the vicinity of a face in a scene, the separation between a picture district and the face
space is registered for all areas in the picture. The consequence of computing the
separation from face space is a face map.
Face detection Techniques and algorithms:
Finding faces:
By color:
If you have access to color images you may use a skin recognition technique for face detection
and the disadvantage is that it is not possible in bad lightening condition
Locating and detecting faces by color:
Motion detection by spatial filtering with an appearance base face model in the form of
neural net. If there are multiple people tracking can be performed by kalman filtering and
time symmetric matching. The main technique in this method is color detection and
tracking using that color.

Fig1 shows the tight clustering of skin color and the top row shows that the regions used to build
the face modules and fig2 shows that the bottom row shows the color distributions plotted in
hue saturation space with 2 component Gaussian overload.
P(x/C) =∑ 𝑝 (
𝑥
𝑗
) 𝑃(𝑗)𝑀
𝑗=1
P (j) corresponds to the prior probability of the data x was generated by component j. Each
mixture component, P(x/j) is a Gaussian with mean variance µj and covariance matrix. Given n
face pixels xi, i=1, 2…n Expectation-Maximization (EM) provides an effective maximum likelihood
algorithm for learning a Gaussian mixture model. Let the some of the probabilities be
Sj = ∑ 𝑃(𝑗/𝑥𝑛
𝑖=1 i)
This color based technique can be equipped with Pentium based Pc and a Matrix Meteor frame
grabber and an active camera which should be minimum 15 frames per second
By Motion Detection:

For detecting and analyzing a video. The following are the steps
Frame detection
Threshold
Noise removal
Add pixels on each motion picture
First we find the difference between the current frame in the video sequence and the previous.
If the difference between the pixels values are greater than (colors used)/10, the movement has
been significant and the pixel is set to black. If the change is less than this threshold, the pixel is
set to white.
In the thresholder image, there may be noise. To remove the noise, we scan the image with
a 3x3 window and remove all black pixels which are isolated in a white area. If the center pixel of
the 3x3 frame is black and less than three of the pixels in the frame are black, we remove the
black center pixel because it is probably noise. Otherwise the pixel remains black. This way we
detect only "large" moving objects.
We use the image to find the upper moving object in the images. If there are three lines with
movement greater than fifteen pixels below each other, we assume this is an object, not just
single pixels with movement. By using the information about how much motion there is on each
line, a point in the middle of the upper moving object is calculated. This is done by calculating the
center of the object within a square of a fixed size (40x40 pixels).
By Blink Detection:
Blinking detection by the technique of space time signal which is unique in different faces.
Blinking is involuntary so it is different among all so this is one advantage in face detection.
Human eyes are positioned in symmetrical and provides a position to normalize the head.
Hardware component used is blinker detector which can be designed. An image is acquired and
subtracted from the previous image and the difference image contains a small boundary outside
the head if the eyes happened to be closed in one of the sequence image then there should be a
roundish change in the sequence of eyes and that should be different.
The difference image is threshold, and a connected components algorithm is run on the
thresholder image. A bounding box is computed for each connected components. A candidate
for an eye must have a bounding box within a particular horizontal and vertical size. Two such
candidates must be detected with a horizontal separation of a certain range of sizes, and little
vertical difference in the vertical separation. When this configuration of two small bounding
boxes is detected, a pair of blinking eyes is hypothesized. The position in the image is determined

from the center of the line between the bounding boxes. The distance to the face is measured
from the separation. This permits to determine the size of a window which is used to extract the
facefrom the image.This simple technique has proven quite reliablefor determining the position
and size of faces.
Track a person using stereo, color and patterning
Designing a system which can track and respond to users face in real time. Background
elimination for depth estimation, color classification for fast tracking, to discriminate face from
other body parts we use the technique called pattern detection. By the reference paper we can
get some more additional information.
http://www.eecs.berkeley.edu/~trevor/papers/1998-021/
Head Detection:
In this process we have two detections mainly face detection and from face we have eyes
detection. This detection will be a 3D detection and we design an algorithm based on that.
Face Detection:
Although it is very easy for humans to locate, recognize and identify faces, there is still no image
processing system available that is capable of solving this task comparably well. To implement a
real-time system, skin-color based approaches have several advantages compared to other
methods. The processing of color information has proven to be much faster than processing of
other facial features. Under constant lighting conditions color is almost invariant against changes
in size, orientation and partial occlusion of the face. For distinguishing the face color from other
image regions either a general color model (based on the distribution of skin colors in a
population) or a user-specific model can be used. We decided to adapt the color model to the
individual user, as several studies showed that this approach is more reliable and robust than the
use of general models.
During initialization, the user's image is analyzed to determine the individual skin color
distribution. An adaptive threshold technique is applied in order to extract image regions with
high probability of face color when compared with a generalized skin-color model. In order to

speed up the segmentation process,a look-up table is generated which relates each color sample
with its corresponding areainside the defined RGBcolor space.However, color information alone
is not sufficient for face segmentation, since the background behind the user may also contain
skin colored objects that could mistakenly be considered to be part of the facial region.
In order to improve the classification process, we make use of a reference image containing only
background objects. Such a reference image is conveniently captured before the user sits down.
By detecting the luminance differences between the current image and the reference image, the
user is easily located as the foreground object. The reference image must be updated during the
entire tracking process by an adaptive algorithm which takes changes within the background
region into account. Regions classified both as skin and foreground are registered as facial
regions. The segmentation result is further improved by applying non-linear filters (e.g. dilation
and erosion) in order to fill holes in large connected regions and to remove small regions. After
the facial region is located, the color, foreground/background and motion information are
combined and evaluated to keep track of the face when the user moves.
Eye Detection and Tracking:
In the second step, the eyes must be found within the defined facial region. As humans
periodically blink to lubricate their eyes, the closing of the eyelids is analyzed to locate the eyes.
The fact that both eyes blink at the same time provides useful information for distinguishing
blinking from other motions. Eye blinking can be detected by analyzing the luminance differences
in successive video images. Within the facial region, three (instead of two) equal-sized, non-
overlapping blocks with the maximal difference between every two subsequent image frames
are detected. This is carried out by calculating the values of the squared frame differences (SFD)
of the blocks. Two blocks are registered as eye regions, if their SFD values are very similar (since
the eyes blink simultaneously) and significantly larger than the SFD value of the third block.
Additionally, the algorithm takes account of geometric relationships of the eyes. The eye regions
are registered and stored as reference patterns for tracking the eye positions when the user
moves.

The tracking algorithm is based on a luminance-adapted block matching technique. In order to
compensate for temporal changes in luminance and size of the eye pattern, both the reference
eye patterns and the extracted eye regions in the previous image frame (temporal patterns) are
used for matching the eye regions in the current image frame. A block with the same size as the
reference pattern is shifted in the defined facial region, and the squared block differences
between the reference pattern and the current block, as well as a weighted difference between
the temporal pattern and the current block are calculated and summed up. The block with the
minimal weighted difference is considered to be the best estimate of the eye region. After
successful matching, the luminance of each reference eye pattern is adjusted by the difference
of the average luminance values of the detected eye region in the current image and in the
reference pattern, respectively, in order to compensate for temporal changes in lighting. Shows
an example of the stored eye patterns. The darkest circular part in the eye pattern marks the
position of the pupils.
Source:
http://web.archive.org/web/20030821151710/http:/atwww.hhi.de/blick/Head_Tracker/head_t
racker.html
Feature Based Face Recognition:
Highlight based techniques use highlights which can be reliably situated crosswise over face
pictures rather than simply the intensities of the pixels over the face identification area. These
elements can incorporate for instance the focuses of the eyes,or the bend of the eyebrows, state
of the lips and jaw and so on. A sample of a fitted model from the IMM database. Likewise with
the pixel power values, the variety of highlight areas and perhaps related neighborhood surface
data, is displayed measurably. At the end of the day, co difference investigation is utilized,
however this time the information vectors are the comparing directions of the arrangement of
elements in every face. The utilization of eigenvector/eigenvalue investigation for shapes is
known as Statistical Shape Modeling (SSM) or Point Distribution Models (PDMs) as initially
proposed by Cootes and Taylor. We first present SSMs and after that go ahead to demonstrate

how SSMs can be utilized to fit to highlight focuses on inconspicuous information, purported
Active Shape Models (ASMs), which presents the thought of utilizing force/composition data
around every point. At last, we depict the basics of speculation of ASMs to incorporate the whole
pixel force/shading data in the district limited by the ASM unitedly, known as Active Appearance
Models (AAMs). AAMs have the ability to allthe while fit to both the likeshape variety of the face
and its appearance (textural properties). A face-cover is made and its shape and appearance is
demonstrated by the face-space. Investigation in the face-space permits us to see the modular
variety and henceforth to integrate likely faces.Onthe off chance that, say,the method of variety
of sexual orientation is learnt then faces can be adjust along sex varieties; comparatively, if the
learnt variety is because of age, occurrences of appearances can be made experience maturing.
ISOMAP complex implanting of PCA face-space of tests from AT&T database.
Left: disseminate of appearances in initial 3 vital parts indicating non-convexity of space.
Right: ISOMAP projection such that Euclidean separations mean geodesic separations in unique
face-space. The non-convexity of intra-class variety is evident.
Viola Jones Face Detection:
The fundamental standard of the Viola-Jones calculation is to check a sub-window equipped for
identifying countenances over a given data picture. The standard picture preparing methodology
would be to rescale the data picture to diverse sizes and afterward run the altered size indicator

through these pictures. This methodology ends up being fairly tedious because of the count of
the distinctive size pictures Contrary to the standard methodology Viola-Jones re scale the
identifier rather than the information picture and run the finder commonly through the picture
– every time with an alternate size. At initial one may suspect both ways to deal with be just as
tedious, yet Viola-Jones have contrived ascaleinvariant identifier that requires the same number
of computations whatever the size. This locator is built utilizing an alleged indispensable picture
and some basic rectangular components reminiscent of Haar wavelets.
Scale Invariant Detector:
The initial step of the Viola-Jones face identification calculation is to transform the data picture
into a necessary picture. This is finished by making every pixel equivalent to the whole aggregate
of all pixels above and to one side of the concerned pixel. This takes into consideration the
computation of the total of all pixels inside any given rectangle utilizing just four qualities. These
qualities are the pixels in the basic picture that match with the edges of the rectangle in the
information picture.
Sum of grey rectangle = D - (B + C) + A

Different types of features
Each feature resultsinasingle value whichis calculatedbysubtractingthe sumof the white rectangle(s)
from the sum of the black rectangle(s)
The Cascade Classifier:
The fundamental rule of the Viola-Jones face discovery calculation is to filter the identifier
ordinarily through the same picture – every time with another size. Regardless of the fact that a
picture ought to contain one or more confronts it is clear that an over the top huge measure of
the assessed sub-windows would in any case be negatives (non-faces). This acknowledgment
prompts an alternate definition of the issue. Instead of discovering appearances, the calculation
ought to dispose of non-countenances. The idea behind this announcement is that it is speedier
to dispose of a non-face than to discover a face. In light of this a locator comprising of stand out
(solid) classifier all of a sudden appears to be wasteful since the assessment time is consistent
regardless of the data. Henceforth the requirement for a full classifier emerges. The fell classifier
is made out of stages eachcontaining a solidclassifier. The employment of every stageis to figure
out if a given sub-window is certainly not a face or possibly a face. At the point when a sub-
window is arranged to be a non-face by a given stage it is quickly tossed. On the other hand a
sub-window named a possibly face is gone on to the following stage in the course. It takes after
that the more stages a given sub-window passes, the higher the chance the sub-window really
contains a face.

The cascade classifier

Haar Like Feature Based Face Detection:
A Haar highlight classifier uses the rectangle fundamental to ascertain the estimation of an
element. The Haar highlight classifier duplicates the heaviness of every rectangle by its territory
and the outcomes are included. A few Haar highlight classifiers forma stage. A stage comparator
totals all the Haar highlight classifier results in a stage and contrasts this summation and a stage
limit. The limit is additionally a steady gotten from the Ada Boost calculation. Every stage does
not have a set number of Haar elements. Contingent upon the parameters of the preparation
information individual stages can have a fluctuating number of Haar components. A haar based
face detection uses less number of stages and features when compared to viola jones detection
method.
Example of haar feature based detection

Results and Discussion:
Face detection using viola Jones detection method
Generating Positive Examples:
So as to prepare the distinctive phases of the fell classifier the Ada Boost calculation requires to
be encouraged with positive samples – that is, pictures of countenances. The appearances
utilized as a part of this undertaking were taken from two distinct database
FERET the Facial Recognition Technology Database. A database regulated and disseminated by
the American National Institute of Standards and Technology. Contains 2413 facialpictures taken
under controlled conditions
LFW Labeled Faces in the Wild. A database made and circulated by The University of
Massachusetts with the mean of concentrating on the issue of unconstrained face
acknowledgment. Contains more than 13.000 facial pictures found on the web. All appearances
in the database were distinguished by a Viola-Jones face identifier.
A Mat lab script called "algo01.m" fit for molding the database pictures into positive cases was
built. The script does the accompanying:
1) Opens a facial picture and changes to dark scale if necessary.
2) Displays the picture and lets the client put a bouncing box around the face.
3) Re scales the face to 24*24 pixels.
4) Saves the re-scaled face as a picture and as a difference standardized information document.

The fluctuation standardization is recommended by Viola-Jones as a mean of diminishing the
impact of distinctive lighting conditions. The picture from the LFW databaseis appeared after the
client has characterized the face.
Generating a positive example
Advantages of Viola-Jones:
1) It is the most appreciated calculations for face discovery in constant
2) The primary point of interest of this methodology is noncompetitive recognition speed
while generally high location precision, tantamount to much slower calculations
3) High exactness. Viola Jones gives precise face identification.
4) Building a course of classifiers which absolutely decreases calculation time while
enhancing recognition precision
5) The Viola and Jones systemfor face identification is a particularly effective technique as
It has a low false positive rate
Disadvantages of Viola Jones:
1) To a great degree long preparing time
2) Constrained head postures
3) Not distinguish dark Faces

Appendices:
% Create a locator object
faceDetector = vision.CascadeObjectDetector;
% Read information picture
I = imread('visionteam.jpg');
% Detect faces
bbox = step(faceDetector, I);
% Create a shape inserter article to draw jumping boxes around location
shapeInserter = vision.ShapeInserter('BorderColor','Custom','CustomBorderColor',[255 255 0]);
% Draw boxes around distinguished confronts and show results
I_faces = step(shapeInserter, I, int32(bbox));
figure, imshow(I_faces), title('Detected faces');
% Create a locator object
bodyDetector = vision.CascadeObjectDetector('UpperBody');
bodyDetector.MinSize = [60 60];
bodyDetector.ScaleFactor = 1.05;
% Read info picture and identify abdominal area
bbox_body = step(bodyDetector, I);
% Draw bouncing boxes
shapeInserter = vision.ShapeInserter('BorderColor','Custom','CustomBorderColor',[255 255 0]);
I_body = step(shapeInserter, I, int32(bbox_body));
figure, imshow(I_body);
%%
bbox_face = zeros(size(bbox_body));
for i=1:length(bbox_body)
Icrop = imcrop(I,bbox_body(i,:));
bbox = step(faceDetector,Icrop);
bbox_face(i,:) = bbox + [bbox_body(i,1:2)- 1 0 0];

end
I_faces2 = step(shapeInserter, I, int32(bbox_face));
figure, imshow(I_faces2);
%%
Icrop = imcrop(I,bbox_body(1,:));
figure;imshow(Icrop);
bbox = step(faceDetector,Icrop);
hold on;rectangle('Position',bbox,'EdgeColor','y');
%%
x = 5;
Irotate = imrotate(Icrop,x);
imshow(Irotate);
bbox = step(faceDetector, Irotate);
on the off chance that bbox > 0
hold on;rectangle('Position',bbox,'EdgeColor','y'); hold off;
end
There are some face detection programs in face detection that can be written in programming
languages like C
Here we are using one of the face detection technique Hybrid approach for face detection and
to run this program you need to install 'Computer vision system toolbox. For more information,
visit http://www.mathworks.in/products/computer-vision/
Prerequisite: Computer vision systemtoolbox
FACE DETECTION:
clear all
clc
%Detect objects using Viola-Jones Algorithm
%To detect Face

FDetect = vision.CascadeObjectDetector;
%Read the input image
I = imread('Image1.jpg');
%Returns Bounding Box values based on number of objects
BB = step(FDetect,I);
figure,
imshow(I); hold on
for i = 1:size(BB,1)
rectangle('Position',BB(i,:),'LineWidth',5,'LineStyle','-','EdgeColor','r');
end
title('Face Detection');
hold off;
%To detect Nose
NoseDetect = vision.CascadeObjectDetector('Nose','MergeThreshold',16);
BB=step(NoseDetect,I);
figure,
imshow(I); hold on
rectangle('Position',BB(i,:),'LineWidth',4,'LineStyle','-','EdgeColor','b');
end
title('Nose Detection');
hold off;
%To denote the object of interest as 'nose', the argument 'Nose' is passed.
vision.CascadeObjectDetector('Nose','MergeThreshold',16);
%The default syntax for Nose detection :
vision.CascadeObjectDetector('Nose');

%Based on the input image, we can modify the default values of the parameters passed
%tovision.CascaseObjectDetector. Here the default value for 'MergeThreshold' is 4.
%When default value for 'MergeThreshold' is used, the result is not correct.
%To avoid multiple detection around an object, the 'MergeThreshold' value can be overridden
%To detect Mouth
MouthDetect = vision.CascadeObjectDetector('Mouth','MergeThreshold',16);
BB=step(MouthDetect,I);
figure,
imshow(I); hold on
rectangle('Position',BB(i,:),'LineWidth',4,'LineStyle','-','EdgeColor','r');
end
title('Mouth Detection');
hold off;
EYE DETECTION:
%To detect Eyes
EyeDetect = vision.CascadeObjectDetector('EyePairBig');
%Read the input Image
I = imread('harry_potter.jpg');
BB=step(EyeDetect,I);
figure,imshow(I);
rectangle('Position',BB,'LineWidth',4,'LineStyle','-','EdgeColor','b');
title('Eyes Detection');
Eyes=imcrop(I,BB);
figure,imshow(Eyes);
end

Conclusion:
In this paper we have developed anovel facedetection and tracking algorithm. We have referred
severalpapers and surveyed many and find some of the best methods in facedetection. We have
used holistic and hybrid approach in this paper that gives more accurate output by detecting the
face features. Also used the viola jones and haar cascade filters in the algorithm for the face
detection.
Acknowledgments:
I thank Professor Dr Ikhlas Abdel Qader for motivation and support for this research paper and
also Haitham for guiding me throughout this project.
References:
[1]M.H. Yang, D. J. Kriegman, and N. Ahuja. Detecting faces in images: A survey. IEEE Trans. on
PAMI, 24(1):34–58, 2002.
[2] P. Viola and M. Jones, ―Rapid object detection using a boosted cascade of simple features‖.
In Proc. of CVPR, 2001.
[3] B C. Zhang and Z. Zhang, A survey of recent advances in face detection. Technical report,
Microsoft Research, 2010.
[4] K.C. Yow and R. Cipolla, “A probabilistic framework for perceptual grouping of features for
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[5]P. Viola and M. Jones, “Rapid object detection was using a boosted cascade of simple
features,” in Proc. Of CVPR, pp. 511–518, 2001.
[6] Paul Viola, Michael Jones, Robust Real-Time Face Detection, International Journal of
Computer Vision 57(2), 137–154, 2004
[7] Paul Viola, Michael J. Jones, Fast Multi-view Face Detection, Mitsubishi Electric Research
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[9] Jigar M. Pandya, Devang Rathod, Jigna J. Jadav,” A Survey of Face Recognition approach”,
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[11] P. Viola and M. Jones, “Robust real-time object detection,” International Journal of
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[12] M. S. Sadri, N. Shams, M. Rahmaty, I. Hosseini, R. Changiz, S. Mortazavian, S. Kheradmand,
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Conference, 2004.
[13] C. Gao and S. Lu, “Novel FPGA based Haar classifier face detection algorithm acceleration,”
In Proceedings of International Conference on Field Programmable Logic and Applications, 2008.
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Anal. Mach. Intell., volume 20, pages 39–51, 1998.
[15] Gary B. Huang, Manu Ramesh, Tamara Berg, Erik Learned-Miller, Labeled Faces in the Wild:
A Database for Studying Face Recognition in Unconstrained Environments, University of
Massachusetts, Amherst, Technical Report 07-49, October 2007. http://vis-
www.cs.umass.edu/lfw/index.html

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