|
Slide 58 -
|
CSE 576, Spring 2008 Face Recognition and Detection 1 Face Recognition and Detection The “Margaret Thatcher Illusion”, by Peter Thompson Computer Vision CSE576, Spring 2008 Richard Szeliski CSE 576, Spring 2008 Face Recognition and Detection 2 Recognition problems What is it?
Object and scene recognition
Who is it?
Identity recognition
Where is it?
Object detection
What are they doing?
Activities
All of these are classification problems
Choose one class from a list of possible candidates CSE 576, Spring 2008 Face Recognition and Detection 3 What is recognition? A different taxonomy from [Csurka et al. 2006]:
Recognition
Where is this particular object?
Categorization
What kind of object(s) is(are) present?
Content-based image retrieval
Find me something that looks similar
Detection
Locate all instances of a given class CSE 576, Spring 2008 Face Recognition and Detection 4 Readings C. Bishop, “Neural Networks for Pattern Recognition”, Oxford University Press, 1998, Chapter 1.
Forsyth and Ponce, Chap 22.3 (through 22.3.2--eigenfaces)
Turk, M. and Pentland, A. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 1991
Viola, P. A. and Jones, M. J. (2004). Robust real-time face detection. IJCV, 57(2), 137–154. CSE 576, Spring 2008 Face Recognition and Detection 5 Sources Steve Seitz, CSE 455/576, previous quarters
Fei-Fei, Fergus, Torralba, CVPR’2007 course
Efros, CMU 16-721 Learning in Vision
Freeman, MIT 6.869 Computer Vision: Learning
Linda Shapiro, CSE 576, Spring 2007 CSE 576, Spring 2008 Face Recognition and Detection 6 Today’s lecture Face recognition and detection
color-based skin detection
recognition: eigenfaces [Turk & Pentland] and parts [Moghaddan & Pentland]
detection: boosting [Viola & Jones] CSE 576, Spring 2008 Face Recognition and Detection 7 Face detection How to tell if a face is present? CSE 576, Spring 2008 Face Recognition and Detection 8 Skin detection Skin pixels have a distinctive range of colors
Corresponds to region(s) in RGB color space
Skin classifier
A pixel X = (R,G,B) is skin if it is in the skin (color) region
How to find this region? skin CSE 576, Spring 2008 Face Recognition and Detection 9 Skin detection Learn the skin region from examples
Manually label skin/non pixels in one or more “training images”
Plot the training data in RGB space
skin pixels shown in orange, non-skin pixels shown in gray
some skin pixels may be outside the region, non-skin pixels inside. CSE 576, Spring 2008 Face Recognition and Detection 10 Skin classifier Given X = (R,G,B): how to determine if it is skin or not?
Nearest neighbor
find labeled pixel closest to X
Find plane/curve that separates the two classes
popular approach: Support Vector Machines (SVM)
Data modeling
fit a probability density/distribution model to each class CSE 576, Spring 2008 Face Recognition and Detection 11 Probability X is a random variable
P(X) is the probability that X achieves a certain value continuous X discrete X called a PDF
probability distribution/density function
a 2D PDF is a surface
3D PDF is a volume CSE 576, Spring 2008 Face Recognition and Detection 12 Probabilistic skin classification Model PDF / uncertainty
Each pixel has a probability of being skin or not skin
Skin classifier
Given X = (R,G,B): how to determine if it is skin or not?
Choose interpretation of highest probability
Where do we get and ?
CSE 576, Spring 2008 Face Recognition and Detection 13 Learning conditional PDF’s We can calculate P(R | skin) from a set of training images
It is simply a histogram over the pixels in the training images
each bin Ri contains the proportion of skin pixels with color Ri
This doesn’t work as well in higher-dimensional spaces. Why not? Approach: fit parametric PDF functions
common choice is rotated Gaussian
center
covariance CSE 576, Spring 2008 Face Recognition and Detection 14 Learning conditional PDF’s We can calculate P(R | skin) from a set of training images
But this isn’t quite what we want
Why not? How to determine if a pixel is skin?
We want P(skin | R) not P(R | skin)
How can we get it?
CSE 576, Spring 2008 Face Recognition and Detection 15 Bayes rule In terms of our problem: What can we use for the prior P(skin)?
Domain knowledge:
P(skin) may be larger if we know the image contains a person
For a portrait, P(skin) may be higher for pixels in the center
Learn the prior from the training set. How? P(skin) is proportion of skin pixels in training set CSE 576, Spring 2008 Face Recognition and Detection 16 Bayesian estimation Bayesian estimation
Goal is to choose the label (skin or ~skin) that maximizes the posterior ↔ minimizes probability of misclassification
this is called Maximum A Posteriori (MAP) estimation likelihood posterior (unnormalized) CSE 576, Spring 2008 Face Recognition and Detection 17 Skin detection results CSE 576, Spring 2008 Face Recognition and Detection 18 This same procedure applies in more general circumstances
More than two classes
More than one dimension
General classification Example: face detection
Here, X is an image region
dimension = # pixels
each face can be thought of as a point in a high dimensional space H. Schneiderman, T. Kanade. "A Statistical Method for 3D Object Detection Applied to Faces and Cars". CVPR 2000 CSE 576, Spring 2008 Face Recognition and Detection 19 Today’s lecture Face recognition and detection
color-based skin detection
recognition: eigenfaces [Turk & Pentland] and parts [Moghaddan & Pentland]
detection: boosting [Viola & Jones] Eigenfaces for recognition Matthew Turk and Alex Pentland
J. Cognitive Neuroscience
1991 CSE 576, Spring 2008 Face Recognition and Detection 21 Linear subspaces Classification can be expensive:
Big search prob (e.g., nearest neighbors) or store large PDF’s
Suppose the data points are arranged as above
Idea—fit a line, classifier measures distance to line What does the v2 coordinate measure? What does the v1 coordinate measure? distance to line
use it for classification—near 0 for orange pts position along line
use it to specify which orange point it is CSE 576, Spring 2008 Face Recognition and Detection 22 Dimensionality reduction Dimensionality reduction
We can represent the orange points with only their v1 coordinates (since v2 coordinates are all essentially 0)
This makes it much cheaper to store and compare points
A bigger deal for higher dimensional problems CSE 576, Spring 2008 Face Recognition and Detection 23 Linear subspaces Consider the variation along direction v among all of the orange points: What unit vector v minimizes var? What unit vector v maximizes var? Solution: v1 is eigenvector of A with largest eigenvalue
v2 is eigenvector of A with smallest eigenvalue CSE 576, Spring 2008 Face Recognition and Detection 24 Principal component analysis Suppose each data point is N-dimensional
Same procedure applies:
The eigenvectors of A define a new coordinate system
eigenvector with largest eigenvalue captures the most variation among training vectors x
eigenvector with smallest eigenvalue has least variation
We can compress the data using the top few eigenvectors
corresponds to choosing a “linear subspace”
represent points on a line, plane, or “hyper-plane”
these eigenvectors are known as the principal components CSE 576, Spring 2008 Face Recognition and Detection 25 The space of faces An image is a point in a high dimensional space
An N x M image is a point in RNM
We can define vectors in this space as we did in the 2D case + = CSE 576, Spring 2008 Face Recognition and Detection 26 Dimensionality reduction The set of faces is a “subspace” of the set of images
We can find the best subspace using PCA
This is like fitting a “hyper-plane” to the set of faces
spanned by vectors v1, v2, ..., vK
any face CSE 576, Spring 2008 Face Recognition and Detection 27 Eigenfaces PCA extracts the eigenvectors of A
Gives a set of vectors v1, v2, v3, ...
Each vector is a direction in face space
what do these look like? CSE 576, Spring 2008 Face Recognition and Detection 28 Projecting onto the eigenfaces The eigenfaces v1, ..., vK span the space of faces
A face is converted to eigenface coordinates by CSE 576, Spring 2008 Face Recognition and Detection 29 Recognition with eigenfaces Algorithm
Process the image database (set of images with labels)
Run PCA—compute eigenfaces
Calculate the K coefficients for each image
Given a new image (to be recognized) x, calculate K coefficients
Detect if x is a face
If it is a face, who is it?
Find closest labeled face in database
nearest-neighbor in K-dimensional space
CSE 576, Spring 2008 Face Recognition and Detection 30 Choosing the dimension K eigenvalues How many eigenfaces to use?
Look at the decay of the eigenvalues
the eigenvalue tells you the amount of variance “in the direction” of that eigenface
ignore eigenfaces with low variance View-Based and Modular Eigenspaces for Face Recognition Alex Pentland, Baback Moghaddam and Thad Starner CVPR’94 CSE 576, Spring 2008 Face Recognition and Detection 32 Part-based eigenfeatures Learn a separate eigenspace for each face feature
Boosts performance of regular eigenfaces Bayesian Face Recognition Baback Moghaddam, Tony Jebara and Alex Pentland
Pattern Recognition
33(11), 1771-1782, November 2000
(slides from Bill Freeman, MIT 6.869, April 2005) CSE 576, Spring 2008 Face Recognition and Detection 34 Bayesian Face Recognition
CSE 576, Spring 2008 Face Recognition and Detection 35 Bayesian Face Recognition
CSE 576, Spring 2008 Face Recognition and Detection 36 Bayesian Face Recognition
Morphable Face Models Rowland and Perrett ’95
Lanitis, Cootes, and Taylor ’95, ’97
Blanz and Vetter ’99
Matthews and Baker ’04, ‘07 CSE 576, Spring 2008 Face Recognition and Detection 38 Morphable Face Model Use subspace to model elastic 2D or 3D shape variation (vertex positions), in addition to appearance variation Shape S Appearance T CSE 576, Spring 2008 Face Recognition and Detection 39 Morphable Face Model
3D models from Blanz and Vetter ‘99 CSE 576, Spring 2008 Face Recognition and Detection 40 Face Recognition Resources Face Recognition Home Page:
http://www.cs.rug.nl/~peterkr/FACE/face.html
PAMI Special Issue on Face & Gesture (July ‘97)
FERET
http://www.dodcounterdrug.com/facialrecognition/Feret/feret.htm
Face-Recognition Vendor Test (FRVT 2000)
http://www.dodcounterdrug.com/facialrecognition/FRVT2000/frvt2000.htm
Biometrics Consortium
http://www.biometrics.org CSE 576, Spring 2008 Face Recognition and Detection 41 Today’s lecture Face recognition and detection
color-based skin detection
recognition: eigenfaces [Turk & Pentland] and parts [Moghaddan & Pentland]
detection: boosting [Viola & Jones] Robust real-time face detection Paul A. Viola and Michael J. Jones
Intl. J. Computer Vision
57(2), 137–154, 2004
(originally in CVPR’2001)
(slides adapted from Bill Freeman, MIT 6.869, April 2005) CSE 576, Spring 2008 Face Recognition and Detection 43 Scan classifier over locs. & scales CSE 576, Spring 2008 Face Recognition and Detection 44 “Learn” classifier from data Training Data
5000 faces (frontal)
108 non faces
Faces are normalized
Scale, translation
Many variations
Across individuals
Illumination
Pose (rotation both in plane and out) CSE 576, Spring 2008 Face Recognition and Detection 45 Characteristics of algorithm Feature set (…is huge about 16M features)
Efficient feature selection using AdaBoost
New image representation: Integral Image
Cascaded Classifier for rapid detection
Fastest known face detector for gray scale images CSE 576, Spring 2008 Face Recognition and Detection 46 Image features “Rectangle filters”
Similar to Haar wavelets
Differences between sums of pixels in adjacent rectangles CSE 576, Spring 2008 Face Recognition and Detection 47 Partial sum
Any rectangle is
D = 1+4-(2+3)
Also known as:
summed area tables [Crow84]
boxlets [Simard98] Integral Image CSE 576, Spring 2008 Face Recognition and Detection 48 Huge library of filters CSE 576, Spring 2008 Face Recognition and Detection 49 Constructing the classifier Perceptron yields a sufficiently powerful classifier
Use AdaBoost to efficiently choose best features
add a new hi(x) at each round
each hi(xk) is a “decision stump” b=Ew(y [x> q]) a=Ew(y [x< q]) x hi(x) q CSE 576, Spring 2008 Face Recognition and Detection 50 Constructing the classifier For each round of boosting:
Evaluate each rectangle filter on each example
Sort examples by filter values
Select best threshold for each filter (min error)
Use sorting to quickly scan for optimal threshold
Select best filter/threshold combination
Weight is a simple function of error rate
Reweight examples
(There are many tricks to make this more efficient.) CSE 576, Spring 2008 Face Recognition and Detection 51 Good reference on boosting Friedman, J., Hastie, T. and Tibshirani, R. Additive Logistic Regression: a Statistical View of Boosting
http://www-stat.stanford.edu/~hastie/Papers/boost.ps
“We show that boosting fits an additive logistic regression model by stagewise optimization of a criterion very similar to the log-likelihood, and present likelihood based alternatives. We also propose a multi-logit boosting procedure which appears to have advantages over other methods proposed so far.” CSE 576, Spring 2008 Face Recognition and Detection 52 Trading speed for accuracy Given a nested set of classifier hypothesis classes
Computational Risk Minimization CSE 576, Spring 2008 Face Recognition and Detection 53 Speed of face detector (2001) Speed is proportional to the average number of features computed per sub-window.
On the MIT+CMU test set, an average of 9 features (/ 6061) are computed per sub-window.
On a 700 Mhz Pentium III, a 384x288 pixel image takes about 0.067 seconds to process (15 fps).
Roughly 15 times faster than Rowley-Baluja-Kanade and 600 times faster than Schneiderman-Kanade. CSE 576, Spring 2008 Face Recognition and Detection 54 Sample results CSE 576, Spring 2008 Face Recognition and Detection 55 Summary (Viola-Jones) Fastest known face detector for gray images
Three contributions with broad applicability:
Cascaded classifier yields rapid classification
AdaBoost as an extremely efficient feature selector
Rectangle Features + Integral Image can be used for rapid image analysis CSE 576, Spring 2008 Face Recognition and Detection 56 Face detector comparison Informal study by Andrew Gallagher, CMU, for CMU 16-721 Learning-Based Methods in Vision, Spring 2007
The Viola Jones algorithm OpenCV implementation was used. (<2 sec per image).
For Schneiderman and Kanade, Object Detection Using the Statistics of Parts [IJCV’04], the www.pittpatt.com demo was used. (~10-15 seconds per image, including web transmission).
CSE 576, Spring 2008 Face Recognition and Detection 57 Schneiderman Kanade Viola Jones CSE 576, Spring 2008 Face Recognition and Detection 58 Today’s lecture Face recognition and detection
color-based skin detection
recognition: eigenfaces [Turk & Pentland] and parts [Moghaddan & Pentland]
detection: boosting [Viola & Jones]
|