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FACE RECOGNITION
Sangeetha:4SO10MCA39 Roofi Nafeesa:4SO10MCA37
Roopa:4SO10MCA38
MATCH!
CONTENTS
Introduction History Biometrics Face Authentication Drawbacks Tests and Results Conclusion
Face Recognition
INTRODUCTION
Face recognition is a computer based security system capable of automatically verifying or identifying a person.
Biometrics identifies or verifies a person based on individual’s physical characteristics by matching the real time patterns against the enrolled ones.
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Face Recognition
HISTORY
The first attempts to do this began in the 1960’s with a semi-automated system. The first attempts to do this began in the 1960’s with a semi-automated system.
In 1970’s Goldstein, Harmon and Lesk created a system of 21 subjective markers such as hair color and lip thickness.
In 1988, when Kirby and Sirovich used a standard linear algebra technique, ‘Principle Component analysis’ that reduced the computation to less than a hundred values to code a normalized face image.
In 1991, scientists finally succeeded in developing real time automated face recognition system.
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Face Recognition
BIOMETRICS
Biometric consists of several authentication techniques based on unique physical characteristics such as face, fingerprints, iris, hand geometry, retina, and voice.
Biometric Technologies fill the role of analyzing and measuring unique biological properties in order to produce unique identifications which is then digitalized and stored
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Face Recognition
Biometrics can be divided into two main classes
Physiological biometrics -related to the shape of the body
Behavioral biometrics -related to the behavior of a person.
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Face Recognition
Physiological biometrics is related to the shape of the body
• Face Recognition
A facial recognition technique is an application of computer for automatically identifying or verifying a person from a digital image or a video frame from a video source.
Fig: Recognition of face from Body
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Face Recognition
Facial recognition technologies have recently developed into two areas
Facial metric Eigen faces.
Fig: Eigen Face.
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Face Recognition
• Finger-scan
A fingerprint is an impression of the friction ridges of all or any part of the finger.
A friction ridge is a raised portion of the on the palmar(palm)or digits (fingers and toes) or plantar (sole) skin, consisting of one or more connected ridge units of friction ridge skin.
Fig: Fingerprint Bitmap
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Face Recognition
• Iris-scan This recognition method uses the iris of the
eye which is colored area that surrounds the
pupil. Iris patterns are unique and are obtained
through video based image acquisition system.
Fig: Image of IRIS.
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Face Recognition
• Retina-scan: It is based on the blood vessel pattern in
the retina of the eye as the blood vessels at the back of the eye have a unique pattern, from eye to eye and person to person.
Fig: Image of Retina
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Face Recognition
• Hand-scan: These techniques include the estimation of
length, width, thickness and surface area of the hand.
Various method are used to measure the hands- Mechanical or optical principle.
Fig: Hand Geometry Scanner Fig: Acquired Image of Hand.
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Face Recognition
Behavioral biometrics is related to the behavior of a person.
• Voice-scan: Voice is also physiological trait because
every person has different pitch, but voice recognition is mainly based on the study of the way a person speaks.
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Face Recognition
• Signature-scan:
The signature dynamics recognition is based on the dynamics of making the signature, rather than a direct comparison of the signature itself afterwards.
There are various kinds of devices used to capture the signature dynamics traditional tablets or special purpose devices.
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Face Recognition
Access Control System using Face Recognition
Face recognition applications are more and more being taken interest in and developed
They are non-intrusive. Biometric data of the faces (photos, videos)
can be easily taken with available devices like cameras.
One biometric data is used in many different environments.
And facial recognition sounds rather interesting in comparison with other biometric technologies.
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Face Recognition
Challenges involved out-of-Plane Rotation: frontal, 45 degree, profile,
upside down Presence of beard, mustache, glasses etc Facial Expressions Occlusions by long hair, hand In-Plane Rotation Image conditions:
Size Lighting condition Distortion Noise Compression
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Face Recognition
Lighting variation Orientation variation (face angle) Size variation Large database Processor intensive Time requirements
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FACE AUTHENTICATION
Face Recognition
MODEL
Access Control System Based on Face Authentication Model
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Image digital
processing
Face Capture devices
Face databas
e
Face detecti
on
ID
ALGORITHMS IN USE
Face Recognition
FACE RECOGNITION MODEL
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Facedetection
Featureextraction
Databaseof enrolled
users
Feature match
Face image
Face id
Face Recognition Processing Flow
Face Recognition
FACE RECOGNITION MODEL CONTD…
Face Detection Feature Extraction Feature Match
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FACE RECOGNITION ALGORITHM
Face Recognition
GEOMETRIC FEATURE-BASED APPROACH
Based on the geometric characteristics of faces
Parts of human faces such as eyes, nose, and mouth are located together with their attributes
Distinguish faces based on information. This approach is quite effective for small
database, with steady lighting and viewpoint.
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Face Recognition
DISADVANTAGES:
Not effective for unstable lighting condition and changing viewpoint.
The scanning technology is not yet reliable. The information extracted is not enough for
an information-rich organ like face.
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Face Recognition
Geometric feature-based approach
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Face Recognition
APPEARANCE-BASED APPROACH
Based on human appearance.. Transforms the face space into subspaces Fewer dimensions Principal Component Analysis (PCA) KLT – Karhunen- Loève Transform.
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Face Recognition
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EIGENFACE ALGORITHM
Face Recognition
Eigenfaces Initialization1.Acquire an initial set of face images
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2.Calculate the eigen
faces from the training set, keeping only the M images that correspond to the highest eigenvalues. These M images define the face space. As new faces are experienced, the eigen faces can be updated or recalculated
Face Recognition
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3. Calculate the corresponding distribution in M-
dimensional weight space for each known individual, by projecting their face images onto the “face space.”
Face Recogniton
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Face Recognition
EIGENFACES RECOGNITION
1. Calculate a set of weights based on the input image and the M eigenfaces by projecting the input image onto each of the eigenfaces.
2. Determine if the image is a face at all by checking to see if the image is sufficiently close to “face space.”
3. If it is a face, classify the weight pattern as either a known person or as unknown.
4. (Optional) Update the eigenfaces and/or weight patterns.
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Face Recognition
PARAMETER BASED FACIAL RECOGNITION:
Facial image is analyzed and reduced to small set of parameters describing prominent facial features
Major features analyzed are: eyes, nose, mouth and cheekbone curvature
These features are then matched to a database Advantage: recognition task is not very expensive Disadvantage: the image processing required is
very expensive and parameter selection must be unambiguous to match an individual’s face
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Face Recognition
TEMPLATE BASED FACIAL RECOGNITION
Salient regions of the facial image are extracted
These regions are then compared on a pixel-by-pixel basis with an image in the database
Advantage is that the image preprocessing is simpler
Disadvantage is the database search and comparison is very expensive
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Face Recognition
OTHER APPROACH
Local Features Analysis (LFA) method. Gabor wavelet-based features method. Local Binary Pattern (LBP) method.
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Face Recognition
THREE-DIMENSIONAL FACE RECOGNITION (3D FACE RECOGNITION)
Three-dimensional geometry of the human face is used.
Higher accuracy than their 2D counterparts Multiple images from different angles from a common
camera may be used to create the 3D model with significant post-processing.
The main technological limitation of 3D face recognition methods is the acquisition of 3D images, which usually requires a range camera.
3D face recognition is still an active research field, though several vendors offer commercial solution.
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Lenovo – Asus – Toshiba
Face Recognition
LENOVO VERIFACE III
User interface of Veriface III, released on Aug 06th 2008. Lenovo has had interesting ads with Robinson and his wife.
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Face Recognition
ASUS SMARTLOGIN
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Face Recognition
TOSHIBA FACE RECOGNITION
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DRAWBACKS
Face Recognition
The security threat posed to Lenovo’s – Asus’s – Toshiba’s products, based on the basis face recognition algorithms and the tests performed on them:
• Face Recognition in comparison with other biometric recognition systems.
• Influences of varied lighting• Influences of image capturing devices• Influences of Image Processing
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Face Recognition
Table 1: State of art of biometric recognition systems
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Face Recognition
Biometric recognition systemsFinger Print TechnologyFace Recognition TechnologyIris TechnologyHand Geometry TechniqueRetina GeometrySpeaker Recognition Technique (voice)Signature Verification Technique
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BYPASS MODEL
Face Recognition
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FAKE FACE
Face Recognition
How to get a target’s image
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Face Recognition
Fake Face Bruteforce
There are several things to concern about in image editing so as the Brute Force to be successful, including:The image’s viewpoint.Lighting effect.
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TESTS AND RESULTS
Face Recognition
2) Asus SmartLogon V1.0.0005
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Face Recognition
1) Lenovo Veriface III
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Face Recognition
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3) Toshiba Face Recognition 2.0.2.32
Face Recognition
RESULT ESTIMATION
Table 2:results of the tests on the Bypass Model
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Face Recognition
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CONCLUSION
•The weak points that might allow one to bypass into the systems of the three big computer manufacturers Lenovo – Asus – Toshiba is to give sufficient evidences that the authentication technologies being used by these three manufacturers are not efficient and secure enough as they are prone to be bypassed putting users’ data at serious risk.
Thank You
Face Recognition
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Questions??