Title: Face Recognition
1Face Recognition
2Introduction
- Why we are interested in face recognition?
- Passport control at terminals in airports
- Participant identification in meetings
- System access control
- Scanning for criminal persons
3Face Recognition
- Face is the most common biometric used by humans
- Applications range from static, mug-shot
verification to a dynamic, uncontrolled face
identification in a cluttered background - Challenges
- automatically locate the face
- recognize the face from a general view point
under different illumination conditions, facial
expressions, and aging effects
4Authentication vs Identification
- Face Authentication/Verification (11 matching)
- Face Identification/recognition(11 matching)
5Applications
- It is not fool proof many have been fooled by
identical twins - Because of these, use of facial biometrics for
identification is often questioned.
6Application
- Video Surveillance (On-line or off-line)
- http//www.crossmatch.com/facial_recognition_solut
ions.html
locates and extracts images from video footage
for identification and verification
7Why is Face Recognition Hard?
8Why is Face Recognition Hard?
9Face Recognition Difficulties
- Identify similar faces (inter-class similarity)
- Accommodate intra-class variability due to
- head pose
- illumination conditions
- expressions
- facial accessories
- aging effects
- Cartoon faces
10Inter-class Similarity
- Different persons may have very similar appearance
Twins
Father and son
11Intra-class Variability
- Faces with intra-subject variations in pose,
illumination, expression, accessories, color,
occlusions, and brightness
12Sketch of a Pattern RecognitionArchitecture
13Example Face Detection
- Scan window over image.
- Classify window as either
- Face
- Non-face
14Profile views
- Schneidermans Test set as an example
15Example Finding skinNon-parametric
Representation of CCD
- Skin has a very small range of (intensity
independent) colors, and little texture - Compute an intensity-independent color measure,
check if color is in this range, check if there
is little texture (median filter) - See this as a classifier - we can set up the
tests by hand, or learn them. - get class conditional densities (histograms),
priors from data (counting) - Classifier is
16(No Transcript)
17Face Detection Algorithm
18Face Recognition
19Face Recognition 2-D and 3-D
20Image as a Feature Vector
- Consider an n-pixel image to be a point in an
n-dimensional space, - Each pixel value is a coordinate of x.
21Nearest Neighbor Classifier
- Rj is the training dataset
- The match for I is R1, who is closer than R2
22Comments
- Sometimes called Template Matching
- Variations on distance function
- Multiple templates per class- perhaps many
training images per class. - Expensive to compute k distances, especially when
each image is big (N dimensional). - May not generalize well to unseen examples of
class. - Some solutions
- Bayesian classification
- Dimensionality reduction
23 Face Recognition Solutions
- Holistic or Appearance-based Face recognition
- EigenFace
- LDA
- Feature-based
24EigenFace
25EigenFace
- Use Principle Component Analysis (PCA) to
determine the most discriminating features
between images of faces. - The principal component analysis or
Karhunen-Loeve transform is a mathematical way of
determining that linear transformation of a
sample of points in L-dimensional space which
exhibits the properties of the sample most
clearly along the coordinate axes.
26PCA
- http//www.cs.otago.ac.nz/cosc453/student_tutorial
s/principal_components.pdf
27More New Techniques in Face Biometrics
- Facial geometry, 3D face recognition
http//www-users.cs.york.ac.uk/nep/research/3Dfac
e/tomh/3DFaceDatabase.html 3D reconstruction
28Skin pattern recognition
- using the details of the skin for authentication
http//pagesperso-orange.fr/fingerchip/biometrics/
types/face.htm
29Facial thermogram
- Facial thermogram requires an (expensive)
infrared camera to detect the facial heat
patterns that are unique to every human being.
Technology Recognition Systems worked on that
subject in 1996-1999. Now disappeared.
http//pagesperso-orange.fr/fingerchip/biometrics/
types/face.htm
30Side effect of Facial thermogram
- can detect lies
- The image on the left shows his normal facial
thermogram, and the image on the right shows the
temperature changes when he lied.
http//pagesperso-orange.fr/fingerchip/biometrics/
types/face.htm
31Smile recognition
- Probing the characteristic pattern of muscles
beneath the skin of the face. - Analyzing how the skin around the subject's mouth
moves between the two smiles. - Tracking changes in the position of tiny wrinkles
in the skin, each just a fraction of a millimetre
wide. - The data is used to produce an image of the face
overlaid with tiny arrows that indicate how
different areas of skin move during a smile.
http//pagesperso-orange.fr/fingerchip/biometrics/
types/face.htm
32Dynamic facial features
- hey track the motion of certain features on the
face during a facial expression (e.g., smile) and
obtain a vector field that characterizes the
deformation of the face.
http//pagesperso-orange.fr/fingerchip/biometrics/
types/face.htm