Face Recognition Using
Neural Networks
Presented By:
Hadis Mohseni
Leila Taghavi
Atefeh Mirsafian
Outline
 Overview
 Scaling Invariance
 Rotation Invariance
 Face Recognition Methods


Multi-Layer Perceptron
Hybrid NN


SOM
Convolutional NN
 Conclusion
2
Overview
3
Scaling Invariance
 Magnifying image while minimizing the loss of
perceptual quality.
 Interpolation methods:


Weighted sum of neighboring pixels.
Content-adaptive methods.
 Edge-directed.
 Classification-based.

Using multilayer neural networks.
 Proposed method: Content-adaptive neural filters
using pixel classification.
4
Scaling Invariance (Cont.)
 Pixel Classification:
0, if x  xav
ADRC( x)  
1, otherwise

Adaptive Dynamic Range Coding (ADRC):

Concatenation of ADRC(x) of all pixels in the window gives the class
code.
If we invert the picture date, the coefficients for the filter should remain
the same ⇒ It is possible to reduce half of the numbers of classes.
Number of classes: 2N-1 for a window with N pixels


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Scaling Invariance (Cont.)
 Content-adaptive neural filters:



The original high resolution, y, and the downscaled, x,
images are employed as the training set. These pairs, (x, y),
are classified using ADRC on the input vector x.
The optimal coefficients are obtained for each class.
The coefficients are stored in the corresponding index of a
look-up-table(LUT).
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Scaling Invariance (Cont.)
 A simple 3-layer feedforward architecture.
 Few neurons in the hidden layer.
 The activation function in the hidden layer
is tanh.
 The neural network can be described as:
Nh
y1   un .(tanh(n .x)  bn )  b0
n 1
 y2, y3 and y4 can be calculated in the same
way by flipping the window simmetrically
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Scaling Invariance (Cont.)
 Pixel classification set reduction
1. Calculate the Euclidian distance of normalized coefficient
vector between each class.
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D   (i ,a i ,b ) 2
i 1
2.
3.
If the distance is below the threshold, combine the classes.
The coefficient can be obtained by training on the combined
data of the corresponding classes.
Repeat step 1 for the new class set , until the threshold is
reached.
8
Scaling Invariance (Cont.)
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Rotation Invariance
 Handling in-plane rotation of face.
 Using a neural network called router.
 The router’s input is the same region that the detector network
will receive as input.
 The router returns the angle of the face.
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Rotation Invariance (Cont.)
 The output angle can be represented by
 Single unit
 1-of-N encoding
 Gaussian output encoding
 An array of 72 output unit is used for proposed method.
 For a face with angle of θ, each output trained to have a value of
cos(θ – i×5o)
 Computing an input face angle as:
71
 71

  outputi  cos(i  5),  outputi  sin(i  5) 
i 0
 i 0

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Rotation Invariance (Cont.)
 Router architecture







Input is 20×20 window
of scaled image.
Router has a single
hidden layer consisting
of a total 100 units.
There are 4 sets of units
in hidden layer.
Each unit connects to a 4×4 region of the input.
Each set of 25 units covers the entire input without overlap.
The activation function for hidden layer is tanh.
The network in trained using the standard error back propagation
algorithm.
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Rotation Invariance (Cont.)


Generating a set of manually labeled example images
Align the labeled faces:
1.
2.
3.
4.
Initializing F, a vector which will be the average position of
each labeled feature over all the training faces.
Each face is aligned with F by computing rotation and scaling.
Transformation can be written as linear functions, we can solve
it for the best alignment.
After iterating these steps a small number of times, the
alignments converge.
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Rotation Invariance (Cont.)

To generate the training set, the faces are rotated to a random
orientation.
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Rotation Invariance (Cont.)
 Empirical results:
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Rotation Invariance (Cont.)
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Face Recognition Methods
 Database:



ORL(Olivetti Research Lab.) Database consists of 10
92×112 different images of 40 distinct subject.
5 image per person for training set and 5 for test.
There are variation of facial expression and facial detail.
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Face Recognition Methods
 Multi-Layer Perceptron:



The training set faces are run through a PCA, and the 200
corresponding eigenvectors (principal components) are found
which can be displayed as eigenfaces.
Each face in the training set can be
reconstructed by a linear combination
of all the principal components.
By projecting the test set images onto
the eigenvector basis, the eigenvector
expansion coefficients can be found.
(a dimensionality reduction!)
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Face Recognition Methods (Cont.)
MLP
 Training classifier using coefficients
of training set images.
 Using variable number of
principal components ranging
from 25 to 200 in different
simulation.
 Repeating simulation 5 times for
each number with random initialization of all parameters in the
MLP and averaging the results for that number.
 The Error Backpropagation learning algorithm was applied
with a small constant learning rate (normally < 0.01)
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Face Recognition Methods (Cont.)
MLP
 Results:
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Face Recognition Methods (Cont.)
 Hybrid NN
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Face Recognition Methods (Cont.)
Hybrid NN
1.
Local Image Sampling
• [ xi W , j W , xi W , j W 1 ,...,xij ,...,xi W , j W 1 , xi W , j W ]
•
[ xij  xi W , j W , xij  xi W , j W 1,...,wij xij ,...,xij  xi W , j W 1, xij  xi W , j W ]
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Face Recognition Methods (Cont.)
Hybrid NN
2. Self-Organizing Map
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Face Recognition Methods (Cont.)
Hybrid NN
mi  [ i1 , i 2 ,...,in ]T   n
mi is a refrence vectorin theinputspaceassignedto each nodein theSOM.
mi (t  1)  mi (t )  hci (t )[x(t )  mi (t )]
hci (t )  h( rc  ri , t )
hci (t ) is a neighborhood function
 rc  ri 2  rt  r
c
i
 
hci   (t ) exp 

0
2
 2 (t ) 


rc is the nodewith theclosest weight vector to the input
 (t ) is a scalar valued learningrate 
 (t ) defines the widthof the kernel
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Face Recognition Methods (Cont.)
Hybrid NN
 SOM image samples corresponding to each node
before training and after training
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Face Recognition Methods (Cont.)
Hybrid NN
3. Convolutional NNs
Invariant to some degree of:


Shift
Deformation
Using these 3 ideas:



Local Receptive Fields
Shared Weights  aiding genaralization
Spatial Subsampling
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Face Recognition Methods (Cont.)
Hybrid NN
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Face Recognition Methods (Cont.)
Hybrid NN
 Network Layers:

Convolutional Layers



Each Layer one or more planes
Each Plane can be considered as a feature map which
has a fixed feature detector that is convolved with the
local window which is scanned over the planes in
previous layer.
Subsampling Layers

Local averaging and subsampling operation
28
Face Recognition Methods (Cont.)
Hybrid NN
 Convolutional and Sampling relations:
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Face Recognition Methods (Cont.)
Hybrid NN
 Simulation Details:



Initial weights are uniformly distributed random numbers in
the range [-2.4/Fi, 2.4/Fi] where Fi is the fan-in neuron i.
Target outputs are -0.8 and 0.8 using the tanh output
activation function.
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Weights are updated after each pattern presentation.
Face Recognition Methods (Cont.)
Hybrid NN
 Expremental Results

Expriment #1:

Variation of the number of output classes
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Face Recognition Methods (Cont.)
Hybrid NN

Variation of the dimentionality of the SOM
32
Face Recognition Methods (Cont.)
Hybrid NN

Substituting the SOM with the KLT

Replacing the CN with an MLP
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Face Recognition Methods (Cont.)
Hybrid NN

The tradeoff between rejection threshold and
recognition accuracy
34
Face Recognition Methods (Cont.)
Hybrid NN

Comparison with other known results on the same
database
35
Face Recognition Methods (Cont.)
Hybrid NN

Variation of the number of training images per person
36
Face Recognition Methods (Cont.)
Hybrid NN
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Face Recognition Methods (Cont.)
 Expriment
#2:
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Face Recognition Methods (Cont.)
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Conclusion
 The results of the face recognition expriments are
greatly influenced by:




The Training Data
The Preprocessing Function
The Type of Network selected
Activation Functions
 A fast, automatic system for face recognition has
been presented which is a combination of SOM
and CN. This network is partial invariant to
translation, rotation, scale and deformation.
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