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Facial Recognition


                     Presented by :
                             J.S.V. Suresh Kumar
                              09131A1223
                             21/01/2013
03/12/13                                           1
Outline
1. Introduction
2. Biometrics
3. History
4. Facial Recognition
5. Implementation
6. How it works
7. Strengths & Weaknesses
8. Applications
9. Conclusion
10. Refrences

03/12/13                         2
Introduction

 Everyday actions are increasingly being handled
  electronically, instead of pencil and paper or face to
  face.

 This growth in electronic transactions results in
  great demand for fast and accurate user
  identification and authentication.


03/12/13                                               3
 Access codes for buildings, banks accounts and
    computer systems often use PIN's for
    identification and security clearences.
   Using the proper PIN gains access, but the user
    of the PIN is not verified. When credit and
    ATM cards are lost or stolen, an unauthorized
    user can often come up with the correct
    personal codes.
   Face recognition technology may solve this
    problem since a face is undeniably connected
    to its owner expect in the case of identical
    twins.

03/12/13                                              4
 A biometric is a unique, measurable characteristic
  of a human being that can be used to automatically
  recognize an individual or verify an individual’s
  identity.
 Biometrics can measure both physiological and
  behavioral characteristics.
 Physiological biometrics:- This biometrics is based
  on measurements and data derived from direct
  measurement of a part of the human body.
 Behavioral biometrics:- this biometrics is based on
  measurements and data derived from an action.
03/12/13                                                5
Types Of Biometrics
     PHYSIOLOGICAL        BEHAVIORAL

a. Finger-scan          a. Voice-scan
b. Facial Recognition   b. Signature-scan
c. Iris-scan            c. Keystroke-scan
d. Retina-scan
e. Hand-scan



03/12/13                                    6
Facial Recognition ???

    It requires no physical interaction on behalf of
     the user.
    It is accurate and allows for high enrolment
     and verification rates.
    It can use your existing hardware
     infrastructure, existing camaras and image
     capture Devices will work with no problems


03/12/13                                                7
History
 In 1960s, the first semi-automated system for facial
  recognition to locate the features(such as eyes, ears,
  nose and mouth) on the photographs.
 In 1970s, Goldstein and Harmon used 21 specific
  subjective markers such as hair color and lip
  thickness to automate the recognition.
 In 1988, Kirby and Sirovich used standard linear
  algebra technique, to the face recognition.


03/12/13                                               8
Facial Recognition
In     Facial recognition   there   are   two   types   of
     comparisons:-

      VERIFICATION- The system compares the given
     individual with who they say they are and gives a yes
     or no decision.

 IDENTIFICATION- The system compares the given
  individual to all the Other individuals in the database
  and gives a ranked list of matches.
03/12/13                                                 9
Contd…
 All identification or authentication technologies
  operate using the following four stages:
 Capture: A physical or behavioural sample is
  captured by the system during Enrollment and
  also in identification or verification process.
 Extraction: unique data is extracted from the
  sample and a template is created.
 Comparison: the template is then compared
  with a new sample.
 Match/non-match: the system decides if the
  features extracted from the new Samples are a
  match or a non match.
03/12/13                                              10
Implementation

     The implementation of face recognition technology
    includes the following four stages:
•   Image acquisition
•   Image processing
•   Distinctive characteristic location
•   Template creation
•   Template matching



03/12/13                                            11
Image acquisition
• Facial-scan technology can acquire faces from almost
  any static camera or video system that generates
  images of sufficient quality and resolution.
• High-quality enrollment is essential to eventual
  verification and identification enrollment images
  define the facial characteristics to be used in all
  future authentication events.




03/12/13                                            12
03/12/13   13
Image Processing
• Images are cropped such that the ovoid facial image
  remains, and color images are normally converted to
  black and white in order to facilitate initial
  comparisons based on grayscale characteristics.
• First the presence of faces or face in a scene must
  be detected. Once the face is detected, it must be
  localized and Normalization process may be required
  to bring the dimensions of the live facial sample in
  alignment with the one on the template.


03/12/13                                            14
Distinctive characteristic location
 All facial-scan systems attempt to match visible facial
  features in a fashion similar to the way people
  recognize one another.
 The features most often utilized in facial-scan
  systems are those least likely to change significantly
  over time: upper ridges of the eye sockets, areas
  around the cheekbones, sides of the mouth, nose
  shape, and the position of major features relative to
  each other.


03/12/13                                               15
Contd..

 Behavioural changes such as alteration of hairstyle,
  changes in makeup, growing or shaving facial hair,
  adding or removing eyeglasses are behaviours that
  impact the ability of facial-scan systems to locate
  distinctive features, facial-scan systems are not yet
  developed to the point where they can overcome
  such variables.



03/12/13                                              16
Template creation




03/12/13                       17
• Enrollment templates are normally created from
   a multiplicity of processed facial images.
 • These templates can vary in size from less than
   100 bytes, generated through certain vendors
   and to over 3K for templates.
 • The 3K template is by far the largest among
   technologies considered physiological biometrics.
 • Larger templates are normally associated with
   behavioral biometrics,



03/12/13                                               18
Template matching
   • It compares match templates against enrollment
     templates.
   • A series of images is acquired and scored against
     the enrollment, so that a user attempting 1:1
     verification within a facial-scan system may have
     10 to 20 match attempts take place within 1 to 2
     seconds.
   • facial-scan is not as effective as finger-scan or iris-
     scan in identifying a single individual from a large
     database, a number of potential matches are
     generally returned after large-scale facial-scan
     identification searches.
03/12/13                                                       19
How Facial Recognition System Works

• Facial recognition software is based on the ability to
  first recognize faces, which is a technological feat in
  itself. If you look at the mirror, you can see that your
  face has certain distinguishable landmarks. These are
  the peaks and valleys that make up the different
  facial features.
• VISIONICS defines these landmarks as nodal points.
  There are about 80 nodal points on a human face.



03/12/13                                                 20
Contd..
     Here are few nodal points that are measured by the
     software.
1.   distance between the eyes
2.   width of the nose
3.   depth of the eye socket
4.   cheekbones
5.   jaw line
6.   chin



03/12/13                                              21
SOFTWARE
 Detection- when the system is attached to a video
  surveilance system, the recognition software searches
  the field of view of a video camera for faces. If there is
  a face in the view, it is detected within a fraction of a
  second. A multi-scale algorithm is used to search for
  faces in low resolution. The system switches to a high-
  resolution search only after a head-like shape is
  detected.
 Alignment- Once a face is detected, the system
  determines the head's position, size and pose. A face
  needs to be turned at least 35 degrees toward the
  camera for the system to register it.
03/12/13                                                  22
 Normalization-The image of the head is scaled and
   rotated so that it can be registered and mapped into
   an appropriate size and pose. Normalization is
   performed regardless of the head's location and
   distance from the camera. Light does not impact the
   normalization process.
  Representation-The system translates the facial data
   into a unique code. This coding process allows for
   easier comparison of the newly acquired facial data to
   stored facial data.
  Matching- The newly acquired facial data is
   compared to the stored data and (ideally) linked to at
   least one stored facial representation.
03/12/13                                              23
 The system maps the face and creates a
       faceprint, a unique numerical code for that face.
       Once the system has stored a faceprint, it can
       compare it to the thousands or millions of
       faceprints stored in a database.
      Each faceprint is stored as an 84-byte file.




03/12/13                                                   24
Strengths

 It has the ability to leverage existing image
  acquisition equipment.
 It can search against static images such as driver’s
  license photographs.
 It is the only biometric able to operate without user
  cooperation.




03/12/13                                             25
Weaknesses

    Changes in acquisition environment
     reduce matching accuracy.
    Changes in physiological characteristics
     reduce matching accuracy.
    It has the potential for privacy abuse due
     to noncooperative enrollment and
     identification capabilities.
03/12/13                                          26
Applications
 Security/Counterterrorism. Access control, comparing
  surveillance images to Know terrorist.
 Day Care: Verify identity of individuals picking up the
  children.
 Residential     Security:   Alert   homeowners       of
  approaching personnel
 Voter verification: Where eligible politicians are
  required to verify their identity during a voting
  process this is intended to stop voting where the vote
  may not go as expected.
 Banking using ATM: The software is able to quickly
  verify a customer’s face.
03/12/13                                               27
Conclusion
• Factors such as environmental changes and mild
  changes in appearance impact the technology to a
  greater degree than many expect.
• For implementations where the biometric system
  must verify and identify users reliably over time,
  facial scan can be a very difficult, but not impossible,
  technology to implement successfully.




03/12/13                                                28
References
• www.biometricgroup.com/wiley
• Biometrics- identify verification in a
  networked world by Samir Nanavati, Micheal
  Thieme and Raj Nanavati.
• History- www.biometrics.gov.




03/12/13                                       29
Thank You…




03/12/13                30

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Face recognition ppt

  • 1. Facial Recognition Presented by : J.S.V. Suresh Kumar 09131A1223 21/01/2013 03/12/13 1
  • 2. Outline 1. Introduction 2. Biometrics 3. History 4. Facial Recognition 5. Implementation 6. How it works 7. Strengths & Weaknesses 8. Applications 9. Conclusion 10. Refrences 03/12/13 2
  • 3. Introduction  Everyday actions are increasingly being handled electronically, instead of pencil and paper or face to face.  This growth in electronic transactions results in great demand for fast and accurate user identification and authentication. 03/12/13 3
  • 4.  Access codes for buildings, banks accounts and computer systems often use PIN's for identification and security clearences.  Using the proper PIN gains access, but the user of the PIN is not verified. When credit and ATM cards are lost or stolen, an unauthorized user can often come up with the correct personal codes.  Face recognition technology may solve this problem since a face is undeniably connected to its owner expect in the case of identical twins. 03/12/13 4
  • 5.  A biometric is a unique, measurable characteristic of a human being that can be used to automatically recognize an individual or verify an individual’s identity.  Biometrics can measure both physiological and behavioral characteristics.  Physiological biometrics:- This biometrics is based on measurements and data derived from direct measurement of a part of the human body.  Behavioral biometrics:- this biometrics is based on measurements and data derived from an action. 03/12/13 5
  • 6. Types Of Biometrics PHYSIOLOGICAL BEHAVIORAL a. Finger-scan a. Voice-scan b. Facial Recognition b. Signature-scan c. Iris-scan c. Keystroke-scan d. Retina-scan e. Hand-scan 03/12/13 6
  • 7. Facial Recognition ???  It requires no physical interaction on behalf of the user.  It is accurate and allows for high enrolment and verification rates.  It can use your existing hardware infrastructure, existing camaras and image capture Devices will work with no problems 03/12/13 7
  • 8. History  In 1960s, the first semi-automated system for facial recognition to locate the features(such as eyes, ears, nose and mouth) on the photographs.  In 1970s, Goldstein and Harmon used 21 specific subjective markers such as hair color and lip thickness to automate the recognition.  In 1988, Kirby and Sirovich used standard linear algebra technique, to the face recognition. 03/12/13 8
  • 9. Facial Recognition In Facial recognition there are two types of comparisons:-  VERIFICATION- The system compares the given individual with who they say they are and gives a yes or no decision.  IDENTIFICATION- The system compares the given individual to all the Other individuals in the database and gives a ranked list of matches. 03/12/13 9
  • 10. Contd…  All identification or authentication technologies operate using the following four stages:  Capture: A physical or behavioural sample is captured by the system during Enrollment and also in identification or verification process.  Extraction: unique data is extracted from the sample and a template is created.  Comparison: the template is then compared with a new sample.  Match/non-match: the system decides if the features extracted from the new Samples are a match or a non match. 03/12/13 10
  • 11. Implementation The implementation of face recognition technology includes the following four stages: • Image acquisition • Image processing • Distinctive characteristic location • Template creation • Template matching 03/12/13 11
  • 12. Image acquisition • Facial-scan technology can acquire faces from almost any static camera or video system that generates images of sufficient quality and resolution. • High-quality enrollment is essential to eventual verification and identification enrollment images define the facial characteristics to be used in all future authentication events. 03/12/13 12
  • 13. 03/12/13 13
  • 14. Image Processing • Images are cropped such that the ovoid facial image remains, and color images are normally converted to black and white in order to facilitate initial comparisons based on grayscale characteristics. • First the presence of faces or face in a scene must be detected. Once the face is detected, it must be localized and Normalization process may be required to bring the dimensions of the live facial sample in alignment with the one on the template. 03/12/13 14
  • 15. Distinctive characteristic location  All facial-scan systems attempt to match visible facial features in a fashion similar to the way people recognize one another.  The features most often utilized in facial-scan systems are those least likely to change significantly over time: upper ridges of the eye sockets, areas around the cheekbones, sides of the mouth, nose shape, and the position of major features relative to each other. 03/12/13 15
  • 16. Contd..  Behavioural changes such as alteration of hairstyle, changes in makeup, growing or shaving facial hair, adding or removing eyeglasses are behaviours that impact the ability of facial-scan systems to locate distinctive features, facial-scan systems are not yet developed to the point where they can overcome such variables. 03/12/13 16
  • 18. • Enrollment templates are normally created from a multiplicity of processed facial images. • These templates can vary in size from less than 100 bytes, generated through certain vendors and to over 3K for templates. • The 3K template is by far the largest among technologies considered physiological biometrics. • Larger templates are normally associated with behavioral biometrics, 03/12/13 18
  • 19. Template matching • It compares match templates against enrollment templates. • A series of images is acquired and scored against the enrollment, so that a user attempting 1:1 verification within a facial-scan system may have 10 to 20 match attempts take place within 1 to 2 seconds. • facial-scan is not as effective as finger-scan or iris- scan in identifying a single individual from a large database, a number of potential matches are generally returned after large-scale facial-scan identification searches. 03/12/13 19
  • 20. How Facial Recognition System Works • Facial recognition software is based on the ability to first recognize faces, which is a technological feat in itself. If you look at the mirror, you can see that your face has certain distinguishable landmarks. These are the peaks and valleys that make up the different facial features. • VISIONICS defines these landmarks as nodal points. There are about 80 nodal points on a human face. 03/12/13 20
  • 21. Contd.. Here are few nodal points that are measured by the software. 1. distance between the eyes 2. width of the nose 3. depth of the eye socket 4. cheekbones 5. jaw line 6. chin 03/12/13 21
  • 22. SOFTWARE  Detection- when the system is attached to a video surveilance system, the recognition software searches the field of view of a video camera for faces. If there is a face in the view, it is detected within a fraction of a second. A multi-scale algorithm is used to search for faces in low resolution. The system switches to a high- resolution search only after a head-like shape is detected.  Alignment- Once a face is detected, the system determines the head's position, size and pose. A face needs to be turned at least 35 degrees toward the camera for the system to register it. 03/12/13 22
  • 23.  Normalization-The image of the head is scaled and rotated so that it can be registered and mapped into an appropriate size and pose. Normalization is performed regardless of the head's location and distance from the camera. Light does not impact the normalization process.  Representation-The system translates the facial data into a unique code. This coding process allows for easier comparison of the newly acquired facial data to stored facial data.  Matching- The newly acquired facial data is compared to the stored data and (ideally) linked to at least one stored facial representation. 03/12/13 23
  • 24.  The system maps the face and creates a faceprint, a unique numerical code for that face. Once the system has stored a faceprint, it can compare it to the thousands or millions of faceprints stored in a database.  Each faceprint is stored as an 84-byte file. 03/12/13 24
  • 25. Strengths  It has the ability to leverage existing image acquisition equipment.  It can search against static images such as driver’s license photographs.  It is the only biometric able to operate without user cooperation. 03/12/13 25
  • 26. Weaknesses  Changes in acquisition environment reduce matching accuracy.  Changes in physiological characteristics reduce matching accuracy.  It has the potential for privacy abuse due to noncooperative enrollment and identification capabilities. 03/12/13 26
  • 27. Applications  Security/Counterterrorism. Access control, comparing surveillance images to Know terrorist.  Day Care: Verify identity of individuals picking up the children.  Residential Security: Alert homeowners of approaching personnel  Voter verification: Where eligible politicians are required to verify their identity during a voting process this is intended to stop voting where the vote may not go as expected.  Banking using ATM: The software is able to quickly verify a customer’s face. 03/12/13 27
  • 28. Conclusion • Factors such as environmental changes and mild changes in appearance impact the technology to a greater degree than many expect. • For implementations where the biometric system must verify and identify users reliably over time, facial scan can be a very difficult, but not impossible, technology to implement successfully. 03/12/13 28
  • 29. References • www.biometricgroup.com/wiley • Biometrics- identify verification in a networked world by Samir Nanavati, Micheal Thieme and Raj Nanavati. • History- www.biometrics.gov. 03/12/13 29