face recognition & biometric systems support vector machines (part 2)
TRANSCRIPT
Face Recognition & Biometric Systems
Plan of the lecture
SVM – main issues repeatedSoft marginMulti-class problemsApplications to face recognitionTraining set optimization
Face Recognition & Biometric Systems
SVM – main issues
Aim: data classificationTwo stages: learning (training) classification
Face Recognition & Biometric Systems
SVM – main issues
Solves linearly separable problemsInput data are transformed mapping into higher dimensions
Training: find optimal hyperplane margin maximisation
Face Recognition & Biometric Systems
SVM – main issues
A function:Data mapping: x(x)Dot product used in all calculationsDot product -> kernel of convolution
No need to know the function
Mn RR :
)()(),( vuvuK
nM
Face Recognition & Biometric Systems
Convolution kernels
Linear
Polynomial
RBF (radial basis functions)
2
2||
),( vu
vu
eK
vuvu ),(K
dK )1(),( vuvu
Face Recognition & Biometric Systems
SVM – main issues
Optimal hyperplane:w0 • x + b0 = 0
for 2D data it is a line
Optimal margin width:
00000
2
||
2
www),bρ(w
Face Recognition & Biometric Systems
SVM – main issues
Optimal hyperplane:
yi – class label i – Lagrange multipliers
(obtained during optimisation)
l
iiii xyw
1
00
Face Recognition & Biometric Systems
SVM – main issues
Lagrange coefficients (): calculated for every vector from the
training set non-zero for support vectors equal zero for the majority of vectors
Training set after the optimisation: support vectors coefficients for every vector number of vectors reduced
Face Recognition & Biometric Systems
SVM – main issues
Classification of a vector:
xr, xs – support vectors from opposite classes
bxxKyxfl
iiii
1),()(
l
isiriii xxKxxKyb
1)],(),([
2
1
Face Recognition & Biometric Systems
Soft margin
Error allowed during the training:
Number of errors minimisedOptimised function must be modified
iii bxwy 1)( 0i
Face Recognition & Biometric Systems
Soft margin
Margin maximisationMinimisation of functional (F – monotonic, convex function):
C – penalty parameter presentation
Constraints:
l
iiCF
1
2
2
1 w
iii bxwy 1)( 0i
Face Recognition & Biometric Systems
Soft margin
Optimisation without the soft margin:
DW TT
2
11)(
),( jijiij xxKyyD
),...,( 1 lT
0Λ 0YΛT ),...,( 1 lT yyY
Face Recognition & Biometric Systems
Soft margin
Optimisation with the soft margin (for F(u) = u2):
)(2
11),(
2
CDW TT
),( jijiij xxKyyD
),...,( 1 lT
0YΛT ),...,( 1 lT yyY10 0
Face Recognition & Biometric Systems
Multi-class problem
Based on two-class problem solved by the SVM
N classes in the training setPossible solutions: base-class approach 1 – N comparisons 1 – 1 comparisons
Face Recognition & Biometric Systems
The base-class approach
The base-class approach one class selected as a base class each class compared with the base
class the strongest response decides
Classification of a single vector: (N – 1) two-class classifications
Face Recognition & Biometric Systems
The base-class approach
Advantages: high speed effective when non-base classes are
easily separable
Disadvantages: problems with separating
non-base classes
Face Recognition & Biometric Systems
1 – N comparisons
Each class compared with the restThe strongest response decidesClassification of a single vector: N two-class classifications
Compared to the base-class approach: more universal (symmetry) comparable speed
Face Recognition & Biometric Systems
1 – 1 comparisons
Each class compared with each otherThe highest precisionClassification of a single vector: N(N – 1)/2 two-class classifications
Very slow method
Face Recognition & Biometric Systems
SVM for face recognition
Detection and verificationFeature vectors comparisonMulti-method fusionOther applications
Face Recognition & Biometric Systems
Face detection
Ellipse detection Generalised Hough Transform a set of face candidates
Normalisation of the candidatesVerification image (as a vector) classified by the
SVM multi-class approach
Face Recognition & Biometric Systems
Feature vectors comparison
Aim: measure similarity between feature vectorsDistance-based similarity: Euclidean distance Mahalanobis distance
Similarity measured by the SVM: two vectors subtracted from each
other create a difference vector difference vector classified
K11
K12
K1n
...
K21
K22
K2n
...
Face Recognition & Biometric Systems
SVM
The sameclass
Differentclasses
K11 - K21
...
K12 - K22
K1n - K2n
Feature vectors comparison
Face Recognition & Biometric Systems
Feature vectors comparison
Good improvement for EBGMEigenfaces not improved similar results to other metrics
Face Recognition & Biometric Systems
Multi-method fusion
Many feature extraction methods
S1
S2
Sn
... S
K1
K2
Kn
...
Two images Feature vectors Similarities
K1
K2
Kn
...
Face Recognition & Biometric Systems
Multi-method fusion
Vector of similarities classified linear kernel polynomial kernel time-consuming classification
SVM applied only for the training linear kernel – weights for the
methods (dimensions stand for methods)
average mean based on the weights
Face Recognition & Biometric Systems
Other applications
Assessment of recognition accuracy vector of sorted similarities
to the elements in the gallery can be used for many images
of the same person
Image quality estimation e.g. elimination of blurred images
Face Recognition & Biometric Systems
SVM – limitations
Constant and small number of classes too much time-consuming for many
classes
Training set: must be representative optimal number of elements
The parameters must be tuned Relevance Vector Machines
Face Recognition & Biometric Systems
Training set optimization
Representative training set: similarity to the classified data universal classification rules difficult to acquire
Real training sets: data acquired automatically low quality, faulty data large number of data
Face Recognition & Biometric Systems
Training set optimization
Selection of available data subset drawn randomly genetic algorithms
Genetic algorithms heuristic optimization technique evolutional strategy population of individuals fitness genetic operators:
selection mutation crossover
Face Recognition & Biometric Systems
Training set optimization
Population drawn
Effectiveness test
Population of training sets
Evolutional operations
Class + Class –
N elements N elements
Individual
+ –
Individual
SVM training
Effectiveness test
Fittness
Face Recognition & Biometric Systems
SVM compared to ANN
Support Vector Machines: more transparent calculations more controllable than neural networks implements various types of ANN useful for image processing
Artificial Neural Networks: more applications (e.g. compression) dedicated hardware solutions
Face Recognition & Biometric Systems
Summary
Soft margin – automatic selectionMulti-class problems: can be solved basing on two-class
problems various approaches
Many possible applications in the area of face recognitionTraining set optimization