Face Detection And Recognization
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- Words: 635
- Pages: 23
- Hardik Dhokai ( 07CE017 )
Guide: Prof. Ashish Patel Computer Department LDRP Institute of Technology and Research
Outline Introduction Goal of face detection Challenges in face detection Different Techniques used for face Detection Pros and Cons of each technique Discussion and Future work
Introduction Face Detection Identify and locate human faces in an image regardless of their Size Expressions Pose Illumination Orientation and occlusion where are the faces, if any?
Cont...
Definition The face detection problem can be defined as : “Given as input an image, which could be a digitized video signal or scanned photograph, determine whether or not there are any human faces in the image if there are, return their location”.
Why Face Detection is Important? First step for most automatic security system First step in many surveillance systems Face is the unique identity of a person Example applications: Automated security systems Intelligence information
Why Face Detection is Difficult? Face Localization: A simplified detection problem with the assumption that an input image contains only one face.
Facial feature detection: To detect the presence and location of features such as eyes, nose, nostrils, eyebrow, mouth,
lips, ears, etc.
Face tracking: Continuously estimate the location and possibly the orientation of face in an image sequence in real time.
Facial expression recognition: concerns the affective states of human’s like happy,sad,disgusted etc.
Cont...
Size: small faces are more difficult to detect. pose: frontal, 45 degree, profile, upside down. Presence or absence of structural beards, moustache, and glasses.
Orientation: Faces can appear in different orientations within the image plane depending on the angle of the camera
and face.
occlusions: faces may be partially occluded by other
Some Examples of Partially Occluded Faces
Approaches to Detect faces Knowledge-based Approach
Feature invariant Approach
Template matching Approach
Appearance-based Approach
Knowledge-Based Approach
Represent a face using a set of human-coded rules Example:
The center part of face has uniform intensity values A face often appears with two eyes that are symmetric to each other, a nose and a mouth
Use these rules to guide the search process
Level 2
Level 1 The input image
Candidates of faces
Level 3
Basic face
Face location&
locations
segmentation
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
Cons:
Difficult to translate human knowledge into rules
Difficult to extend this approach to detect faces in different poses:
Feature-Based Approach
Detect facial features (eyes, nose, mouth, etc) first
Facial features: edge, intensity, shape, texture, skin color, etc
Aim to detect invariant features
Group features into candidates and verify them
Feature-Based Approach : Summary
Pros:
Features are invariant to pose and orientation change
Cons:
Difficult to locate facial features due to several corruption (illumination, occlusion) Difficult to detect features in complex background
Template Matching Approach
Store a template Predefined: based on edges or regions
Deformable: based on outline of face
Templates are handcoded (not learned)
Use correlation to locate faces
Template-Based Approach : Summary
Pros:
It is simple to implement
Cons:
Templates needs to be initialized near the face images Difficult to enumerate templates for different poses (similar to knowledgebased methods)
Appearance-Based Approach Uses machine learning algorithms. Fast and fairly robust techniques. Train a classifier using positive examples of faces Various Methods: Neural model Support vector machine (SVM) Adaboost Hidden Markov Model Distribution Method
CONCLUSION - Face detection is still in a learning mode -
- We hope that in future we will have robust Approaches/methods to detect the face
-
algorithms
Web Resources Face detection home page http://www.facedetection.com/ http://vasc.ri.cmu.edu/NNFaceDetector/#upright
http://vision.ucsd.edu/kriegman-grp/research/ptrack/index.html http://www.cvpr.org/2006/index.html
http://web.archive.org/web/20030602192115/http://www.cs.ru
References
Guide: Prof. Ashish Patel Computer Department LDRP Institute of Technology and Research
Outline Introduction Goal of face detection Challenges in face detection Different Techniques used for face Detection Pros and Cons of each technique Discussion and Future work
Introduction Face Detection Identify and locate human faces in an image regardless of their Size Expressions Pose Illumination Orientation and occlusion where are the faces, if any?
Cont...
Definition The face detection problem can be defined as : “Given as input an image, which could be a digitized video signal or scanned photograph, determine whether or not there are any human faces in the image if there are, return their location”.
Why Face Detection is Important? First step for most automatic security system First step in many surveillance systems Face is the unique identity of a person Example applications: Automated security systems Intelligence information
Why Face Detection is Difficult? Face Localization: A simplified detection problem with the assumption that an input image contains only one face.
Facial feature detection: To detect the presence and location of features such as eyes, nose, nostrils, eyebrow, mouth,
lips, ears, etc.
Face tracking: Continuously estimate the location and possibly the orientation of face in an image sequence in real time.
Facial expression recognition: concerns the affective states of human’s like happy,sad,disgusted etc.
Cont...
Size: small faces are more difficult to detect. pose: frontal, 45 degree, profile, upside down. Presence or absence of structural beards, moustache, and glasses.
Orientation: Faces can appear in different orientations within the image plane depending on the angle of the camera
and face.
occlusions: faces may be partially occluded by other
Some Examples of Partially Occluded Faces
Approaches to Detect faces Knowledge-based Approach
Feature invariant Approach
Template matching Approach
Appearance-based Approach
Knowledge-Based Approach
Represent a face using a set of human-coded rules Example:
The center part of face has uniform intensity values A face often appears with two eyes that are symmetric to each other, a nose and a mouth
Use these rules to guide the search process
Level 2
Level 1 The input image
Candidates of faces
Level 3
Basic face
Face location&
locations
segmentation
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
Cons:
Difficult to translate human knowledge into rules
Difficult to extend this approach to detect faces in different poses:
Feature-Based Approach
Detect facial features (eyes, nose, mouth, etc) first
Facial features: edge, intensity, shape, texture, skin color, etc
Aim to detect invariant features
Group features into candidates and verify them
Feature-Based Approach : Summary
Pros:
Features are invariant to pose and orientation change
Cons:
Difficult to locate facial features due to several corruption (illumination, occlusion) Difficult to detect features in complex background
Template Matching Approach
Store a template Predefined: based on edges or regions
Deformable: based on outline of face
Templates are handcoded (not learned)
Use correlation to locate faces
Template-Based Approach : Summary
Pros:
It is simple to implement
Cons:
Templates needs to be initialized near the face images Difficult to enumerate templates for different poses (similar to knowledgebased methods)
Appearance-Based Approach Uses machine learning algorithms. Fast and fairly robust techniques. Train a classifier using positive examples of faces Various Methods: Neural model Support vector machine (SVM) Adaboost Hidden Markov Model Distribution Method
CONCLUSION - Face detection is still in a learning mode -
- We hope that in future we will have robust Approaches/methods to detect the face
-
algorithms
Web Resources Face detection home page http://www.facedetection.com/ http://vasc.ri.cmu.edu/NNFaceDetector/#upright
http://vision.ucsd.edu/kriegman-grp/research/ptrack/index.html http://www.cvpr.org/2006/index.html
http://web.archive.org/web/20030602192115/http://www.cs.ru
References
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