full term paper of ai
TRANSCRIPT
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TERM PAPER
OF
ARTIFICIAL INTELLIGENCE
TOPIC: FACE RECOGNITION
DOS: 20th oct. 2009
SUBMITTED TO:
MISS BALJINDER
SUBMITTED BY:
ARCHANA SINHA
ROLLNO: RH1801B57
REG.NO:10807781
BRANCH-B.TECH(CSE)
SECTION: H1801
GROUP-G2
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TOPIC: FACE RECOGNITION
CONTENTS:-----------
INTRODUCTION
WHAT IS FACE RECOGNITION??????
j BIOMETRIC
j 3D FACE RECOGNITION
j Face detection
j Image processing
HISTORY OF FACE RECOGNITION EARLY DEVELOPMENT OF FACE RECOGNITION
STEPS REQUIRED IN FACIAL RECOGNITION
TECHNIQUES USED BY FACE RECOGNITION
TECHNOLOGY TREND OF FACE RECOGNITION
WHY DO HUMAN USE FACE RECOGNITION
ADVANTAGE OF FACIAL RECOGNITION
TECHNICIAL DIFFICULTIESWITH FACE RECOGNITION
HUMAN DIFFICULTIESWITH FACE RECOGNITION
REFERENCE
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INTRODUCTION
AI is a branch of computer science concerned with teaching computers to think. It
has had some success in areas like static and moving image recognition but less soin for example playing Chess. It has taken over 40 years to create computer Chess
players able to beat Chess Grandmasters, but that's down to faster CPUs not
software.
In computer games, AI techniques are used to find
shortest paths through complex environments anticipate human player's moves and
fight like a human opponent, adapting to your every move.
A facial recognition system is a computer application
for automatically identifying or verifying a person from a digital image or a videoframe from a video source. One of the ways to do this is by comparing selected
facial features from the image and a facial database. Facial database uses
information regarding to detection of face.
Face recognition is not perfect and struggles to
perform under certain conditions. Ralph Gross, a researcher at the Carnegie Mellon
Robotics Institute, describes one obstacle related to the viewing angle of the face:
"Face recognition has been getting pretty good at full frontal faces and 20 degrees
off, but as soon as you go towards profile, there've been problems."
Early face-detection algorithms focused on the
detection of frontal human faces, whereas newer algorithms attempt to solve themore general and difficult problem of multi-view face detection. That is, the
detection of faces that are either rotated along the axis from the face to the observer
(in-plane rotation), or rotated along the vertical or left-right axis (out-of-plane
rotation), or both.
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WHAT IS AI????
AI is a branch of computer science concerned with teaching computers to think. It
has had some success in areas like static and moving image recognition but less so
in for example playing Chess. It has taken over 40 years to create computer Chess
players able to beat Chess Grandmasters, but that's down to faster CPUs notsoftware.
In computer games, AI techniques are used to find shortest paths
through complex environments anticipate human player's moves and fight like ahuman opponent, adapting to your every move.
WHAT IS FACE RECOGNITION??????
A facial recognition system is a computer application for automatically identifying
or verifying a person from a digital image or a video frame from a video source.
One of the ways to do this is by comparing selected facial features from the image
and a facial database. Facial database uses information regarding to detection of
face.
PICTURES REGARDING TO FACE RECOGNITION:-
HISTORY OF FACE RECOGNITION
Face recognition is as old as computer vision, both because of the practical
importance of the topic and theoretical interest from cognitive scientists.
Despite the fact that other methods of can be more accurate, face recognition
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has always remains a major focus of research because of its non-invasive
nature and because it is people's primary method of person identification.
Perhaps the most famous early example of a face recognition system is due
to Kohonen who demonstrated that a simple neural net could perform face
recognition for aligned and normalized face images. The type of network he
employed computed a face description by approximating the eigenvectors of
the face image's autocorrelation matrix; these eigenvectors are now known
as `eigenfaces.'
Kirby and Sirovich (1989) later introduced an algebraic manipulation which
made it easy to directly calculate the eigenfaces, and showed that fewer than
100 were required to accurately code carefully aligned and normalized face
images.
Turk and Pentland (1991) then demonstrated that the residual error when
coding using the eigenfaces could be used both to detect faces in cluttered
natural imagery, and to determine the precise location and scale of faces inan image. They then demonstrated that by coupling this method for detecting
and localizing faces with the eigenface recognition method, one could
achieve reliable, real-time recognition of faces in a minimally constrained
environment.
This demonstration that simple, real-time pattern recognition techniques
could be combined to create a useful system sparked an explosion of interest
in the topic of face recognition.
STEPS REQUIRED IN FACIAL RECOGNITION1. Capture image:-First, an image of the face is acquired. This acquisition can
be accomplished by digitally scanning an existing photograph or by using an
electro-optical camera to acquire a live picture of a subject. As video is a rapid
sequence of individual still images, it can also be used as a source of facial
images. 2. Find face in image:-for finding face in image the Second step, software is
employed to detect the location of any faces in the acquired image. This task is
difficult, and often generalized patterns of what a face ³looks like´ (two eyes
and a mouth set in an oval shape) are employed to pick out the faces. 3. Extract features :-Once the facial detection software has targeted a face, it
can be analyzed. As noted in slide three, facial recognition analyzes the spatial
geometry of distinguishing features of the face. Different vendors use different
method to extract the identifying features of a face. Thus, specific details on the
methods are proprietary. The most popular method is called Principle
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Components Analysis (PCA), which is commonly referred to as the eigenface
method.
4. Compare templates:-The fourth step is to compare the template generated
in step three with those in a database of Known faces. In an identification
application, this process yields scores that indicate how closely the generatedtemplate matches each of those in the database. In a verification application,
the generated template is only compared with one template in the database ±
that of the claimed identity.
5. Declare matches The final step is determining whether any scores produced
in step four are high enough to declare a match. The rules governing the
declaration of a match are often configurable by the end user, so that he or she
can determine how the facial recognition system should behave based on
security and operational considerations.ind face in image
FIG:-STEPS IN FACE RECOGNITION
BIOMETRIC:-DEFINITION OF BIOMETRIC:-
Any automatically measurable, robust and distinctive physical characteristic or
personal
trait that can be used to identify an individual or verify the claimed identity .
Basically it is used for authentication and verification
Biometrics are used for human recognition which consists of identification
and verification .
Examples of Biometrics IRIS SCAN
RETI NAL SCAN
SPEAKER / VOICE
FI NGERPRI NT
HAND / FI NGER GEOMETRY
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SIG NATURE VERIFICATIO N
KEYSTROKE DY NAMICS
OTHER ESOTERIC BIOMETRICS
GAIT
EAR ODOR
IMAGE PROCESSINGIMAGE PROCESSI NG is any form of signal processing for which the input is
animage, such as photographs or frames of video; the output of image processing
can be either an image or a set of characteristics or parameters related to the image.
IMAGE PROCESSING OPERATIONS ARE:
Euclidean geometry transformations such as enlargement, reduction,
and rotation
Color corrections such as brightness and contrast adjustments, quantization,
or color translation to a different color space
Digital compositing or optical compositing (combination of two or more
images). Used in film-making to make a "matte"
Interpolation, demosaicing, and recovery of a full image from a raw image
format using a Bayer filter pattern
Image registration, the alignment of two or more images
Image differencing and morphing Image recognition, for example, extract the text from the image by using optical
character recognition
Image segmentation
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High dynamic range imaging by combining multiple images
Geometric hashing for 2-D object recognition with affine invariance
Three-dimensional face recognition :-
j (3D face recognition) is a modality of facial recognition methods in which
the three-dimensional geometry of the human face is used.
j 3D face recognition methods can achieve significantly higher accuracy than
their 2D counterparts, rivaling fingerprint recognition.
j 3D face recognition has the potential to achieve better accuracy than its 2D
counterpart by measuring geometry of rigid features on the face. This avoids
such pitfalls of 2D face recognitionalgorithms as change in lighting,
different facial expressions, make-up and head orientation.
j Another approach is to use the 3D model to improve accuracy of traditional
image based recognition by transforming the head into a known view.
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j most range scanners acquire both a 3D mesh and the corresponding texture.
This allows combining the output of pure 3D matchers with the more
traditional 2D face recognition algorithms, thus yielding better performance
j The main technological limitation of 3D face recognition methods is the
acquisition of 3D images, which usually requires a range camera. This isalso a reason why 3D face recognition methods have emerged significantly
later (in the late 1980s) than 2D methods. Recently commercial solutions
have implemented depth perception by projecting a grid onto the face and
integrating video capture of it into a high resolution 3D model.
j This allows for good recognition accuracy with low cost off-the-
shelf components.
FACE DETECTION
j Face detection is a computer technology that determines the locations andsizes of human faces in arbitrary (digital) images. It detects facial features
and ignores anything else, such as buildings, trees and bodies.
j Face detection can be regarded as a specific case of object-class detection; In
object-class detection, the task is to find the locations and sizes of all objects
in an image that belong to a given class. Examples include upper torsos,
pedestrians, and cars.
j In face detection, one does not have this additional information.
j Early face-detection algorithms focused on the detection of frontal humanfaces, whereas newer algorithms attempt to solve the more general and
difficult problem of multi-view face detection. That is, the detection of faces
that are either rotated along the axis from the face to the observer (in-plane
rotation), or rotated along the vertical or left-right axis (out-of-plane
rotation), or both.
. TECHNIQUES USED BY FACE RECOGNITION
Face recognition uses mainly the following techniques: ----------------- Facial geometry: uses geometrical characteristics of the face. May use
several cameras to get better accuracy (2D, 3D...)
Skin pattern recognition (Visual Skin Print)
Facial thermogram: uses an infrared camera to map the face temperatures
Smile: recognition of the wrinkle changes when smiling
Compare tem
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TECHNOLOGY TREND OF FACE RECOGNITION
EIGENFACE METHOD:- This technology re-constructs a face by
superimposing a set
of so-called ³eigenface´. The similarity of two facial images is determined based
on the coefficient of the relevant eigenfaces
Local Feature Analysis This technology locally uses the eigenface method for
a few
single parts of the face (e.g. eyes, nose, mouth) and additionally determines their
geometric proportions to each other.
Dynamic Local Feature Analysis (³DLFA´):- Based on the neural
network-based classification algorithm, the face is represented by an ³dynamic´
edge analyzing the facial shape and texture and thus basing the comparison on up
to 108 characteristics.
Why do human use face recognition? Universal. Everyone has a face, everyone can enrol.
Non-intrusive. A non-contact verification process, similar to having a photo
taken.
Incredibly Fast. Responsive software ³finds´ the user¶s face within a frame
and starts making comparisons instantly. Sensors don¶t need to be adjusted for
height; spectacles and safety gloves don¶t need to be removed; operatives don¶tneed to touch a scanner or present their face in a particular way. Verification
takes approximately 1.5 seconds.
Accurate. Better than humans at verifying identity, and able to work 24 hours a
day.
Dependable. Successfully deployed in challenging real-life environments,
overcoming usual biometric obstacles such as dust, dirt, grease, variable lighting
and user co-operation. Aurora systems are in use in over 940 construction
locations in the UK and Middle East, processing over fifteen million clocking
actions a year.
Transparent. Face recognition is the only biometric where the transaction
records can be visually confirmed.
ADVANTAGE OF FACIAL RECOGNITION pj It is mainly used in airports were it will recognize the face and we can avoid
some unwanted terrorist.
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j Uses faces, which are public
j Involves non-intrusive, contact-free process
j Uses legacy databases
j Integrates with existing surveillance systems
TECHNICIAL DIFFICULTIES WITH FACE RECOGNITION
1. Finding Faces
y Uncontrolled background
y Subject¶s non-cooperation
Subject not looking at camera
Subject wearing hat, sunglasses, etc.
y Moving target
2. Identifying Faces
y Uncontrolled environmental conditions
Lighting (shadows, glare)
Camera angle
Image resolution
Human Difficulties with Facial Recognition
1. Inherent Operator Limitations
y Humans are not good at recognizing faces of
people they do not know
2. Operator Overload Vast amounts of information
Limited attention span
Limited accuracy
3. Operator Reliability
y Dedication
y Honesty
WEAKNESSES OF FACE RECOGNITION:-
j Face recognition is not perfect and struggles to perform under certain
conditions. Ralph Gross, a researcher at the Carnegie Mellon Robotics
Institute, describes one obstacle related to the viewing angle of the face:
"Face recognition has been getting pretty good at full frontal faces and 20
degrees off, but as soon as you go towards profile, there've been problems."
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j face recognition does not work well include poor lighting, sunglasses, long
hair, or other objects partially covering the subject¶s face, and low resolution
images.
j Another serious disadvantage is that many systems are less effective if facial
expressions vary. Even a big smile can render in the system less effectively.For instance: Canada now allows only neutral facial expressions in passport
photos.