18-08-2011, 09:35 AM
Presented By:
Suvigya Tripathi
Ankit V. Gupta
[attachment=15342]
FACE RECOGNITOIN TECHNIQUE
INTRODUCTION
Steps:
Face Detection: differentiate a human face from the background of the image or a real time video.
Feature Detection: record its features.
Face Recognition: Compare it to a data base.
BLOCK DIAGRAM
WHAT IS FACE DETECTION
Technique employed to distinguish a Human face from the rest of the background of the image.
THE HISTORY
During 1964 and 1965, Bledsoe, along with Helen Chan and Charles Bisson , worked on using the computer to recognize human faces.
He was proud of this work, but because the funding was provided by an unnamed intelligence agency that did not allow much publicity, little of the work was published.
IMPORTANCE OF FACE DETECTION
The first step for any automatic face recognition system system.
First step in many Human Computer Interaction systems.
Expression Recognition
Cognitive State/Emotional State Recognition
First step in many surveillance and security systems.
Video coding
Automatic Target Recognition(ATR)
CHALLENGES
In – Plane Rotation
Out – Plane Rotation
Lighting
Aging Effects
Facial Expressions
Face Covered by
long Hairs or Hand.
CHALLENGES
2D – IMAGE SCAN
Different Approaches:
Knowledge Based Approach
Feature Invariant Method
Template Matching Method
KNOWLEDGE-BASED APPROACH
It uses human-coded rules to model facial features, such as two symmetric eyes, a nose in the middle and a mouth underneath the nose.
KNOWLEDGE-BASED APPROACH-SUMMARY
Pros:
Easy to come up with simple rules
Based on the coded rules, facial features in an input image are extracted first, and face candidates are identified
Work well for face localization in uncluttered background
Cons:
Difficult to translate human knowledge into rules precisely: detailed rules fail to detect faces and general rules may find many false positives
Difficult to extend this approach to detect faces in different poses: implausible to enumerate all the possible cases
FEATURE INVARIANT METHOD
Feature invariant methods try to find facial features which are invariant to pose, lighting condition or rotation.
Skin colors, edges and shapes fall into this category.
FEATURE INVARIANT METHOD-NODAL POINT ANALYSIS
Every face has numerous, distinguishable landmarks, the different peaks and valleys that make up the face
Distance between the eyes
Width of the nose
Depth of the eye sockets
The shape of the cheekbones
The length of the jaw line
FEATURE INVARIANT METHOD-SUMMARY
Pros:
Features are invariant to pose and change in orientation.
Cons:
Difficult to locate facial features due to several corruption (illumination, noise, occlusion)
Difficult to detect features in complex background
TEMPLATE MATCHING METHOD
Template matching methods calculate the correlation between a test image and a pre-selected facial templates.
TEMPLATE MATCHING METHOD-SUMMARY
Pros:
Simple
Cons:
Templates needs to be initialized near the face images
Difficult to enumerate templates for different poses (similar to knowledge-based methods)
BIOMETRICS SKIN TEXTURE ANALYSIS
Using skin color to find face segments is a vulnerable technique.
Non-animate objects with
the same color as skin can
be picked up since the
technique uses color
segmentation.
Then the face can be picked up using any of the approaches.
SKIN TEXTURE ANALYSIS: ADVANTAGES
Lack of restriction to orientation or size of faces.
A good algorithm can handle complex backgrounds.
It is relatively insensitive to changes in expression, including blinking, frowning or smiling
Has the ability to compensate for mustache or beard growth and the appearance of eyeglasses.
APPLICATIONS
Security measure at ATM’s
Digital Cameras
Public Surveillance (CCTV’s) at
Airports, Hospitals, etc.
Televisions and computers can
save energy by reducing the
brightness.
FACE RECOGNITION:OVERVIEW
A set of two task:
Face Identification: Given a face image that belongs to a person in a database, tell whose image it is.
Face Verification: Given a face image that might not belong to the database, verify whether it is from the person it is claimed to be in the database.
Suvigya Tripathi
Ankit V. Gupta
[attachment=15342]
FACE RECOGNITOIN TECHNIQUE
INTRODUCTION
Steps:
Face Detection: differentiate a human face from the background of the image or a real time video.
Feature Detection: record its features.
Face Recognition: Compare it to a data base.
BLOCK DIAGRAM
WHAT IS FACE DETECTION
Technique employed to distinguish a Human face from the rest of the background of the image.
THE HISTORY
During 1964 and 1965, Bledsoe, along with Helen Chan and Charles Bisson , worked on using the computer to recognize human faces.
He was proud of this work, but because the funding was provided by an unnamed intelligence agency that did not allow much publicity, little of the work was published.
IMPORTANCE OF FACE DETECTION
The first step for any automatic face recognition system system.
First step in many Human Computer Interaction systems.
Expression Recognition
Cognitive State/Emotional State Recognition
First step in many surveillance and security systems.
Video coding
Automatic Target Recognition(ATR)
CHALLENGES
In – Plane Rotation
Out – Plane Rotation
Lighting
Aging Effects
Facial Expressions
Face Covered by
long Hairs or Hand.
CHALLENGES
2D – IMAGE SCAN
Different Approaches:
Knowledge Based Approach
Feature Invariant Method
Template Matching Method
KNOWLEDGE-BASED APPROACH
It uses human-coded rules to model facial features, such as two symmetric eyes, a nose in the middle and a mouth underneath the nose.
KNOWLEDGE-BASED APPROACH-SUMMARY
Pros:
Easy to come up with simple rules
Based on the coded rules, facial features in an input image are extracted first, and face candidates are identified
Work well for face localization in uncluttered background
Cons:
Difficult to translate human knowledge into rules precisely: detailed rules fail to detect faces and general rules may find many false positives
Difficult to extend this approach to detect faces in different poses: implausible to enumerate all the possible cases
FEATURE INVARIANT METHOD
Feature invariant methods try to find facial features which are invariant to pose, lighting condition or rotation.
Skin colors, edges and shapes fall into this category.
FEATURE INVARIANT METHOD-NODAL POINT ANALYSIS
Every face has numerous, distinguishable landmarks, the different peaks and valleys that make up the face
Distance between the eyes
Width of the nose
Depth of the eye sockets
The shape of the cheekbones
The length of the jaw line
FEATURE INVARIANT METHOD-SUMMARY
Pros:
Features are invariant to pose and change in orientation.
Cons:
Difficult to locate facial features due to several corruption (illumination, noise, occlusion)
Difficult to detect features in complex background
TEMPLATE MATCHING METHOD
Template matching methods calculate the correlation between a test image and a pre-selected facial templates.
TEMPLATE MATCHING METHOD-SUMMARY
Pros:
Simple
Cons:
Templates needs to be initialized near the face images
Difficult to enumerate templates for different poses (similar to knowledge-based methods)
BIOMETRICS SKIN TEXTURE ANALYSIS
Using skin color to find face segments is a vulnerable technique.
Non-animate objects with
the same color as skin can
be picked up since the
technique uses color
segmentation.
Then the face can be picked up using any of the approaches.
SKIN TEXTURE ANALYSIS: ADVANTAGES
Lack of restriction to orientation or size of faces.
A good algorithm can handle complex backgrounds.
It is relatively insensitive to changes in expression, including blinking, frowning or smiling
Has the ability to compensate for mustache or beard growth and the appearance of eyeglasses.
APPLICATIONS
Security measure at ATM’s
Digital Cameras
Public Surveillance (CCTV’s) at
Airports, Hospitals, etc.
Televisions and computers can
save energy by reducing the
brightness.
FACE RECOGNITION:OVERVIEW
A set of two task:
Face Identification: Given a face image that belongs to a person in a database, tell whose image it is.
Face Verification: Given a face image that might not belong to the database, verify whether it is from the person it is claimed to be in the database.
