Face Recognition Using Neural Networks Presented By: Hadis Mohseni Leila Taghavi Atefeh Mirsafian.
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Transcript of Face Recognition Using Neural Networks Presented By: Hadis Mohseni Leila Taghavi Atefeh Mirsafian.
Face Recognition Using Neural Networks
Presented By:Hadis MohseniLeila Taghavi
Atefeh Mirsafian
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Outline
Overview Scaling Invariance Rotation Invariance Face Recognition Methods
Multi-Layer Perceptron Hybrid NN
SOM Convolutional NN
Conclusion
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Overview
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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.
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Scaling Invariance (Cont.)
Pixel Classification: 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
otherwise 1,
x x if ,0)( avxADRC
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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:
y2, y3 and y4 can be calculated in the same way by flipping the window simmetrically
hN
nnnn bbxuy
101 ))..(tanh(
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Scaling Invariance (Cont.)
Pixel classification set reduction1. Calculate the Euclidian distance of normalized coefficient
vector between each class.
2. 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.
3. Repeat step 1 for the new class set , until the threshold is reached.
2,
9
1, )( bi
iaiD
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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
0
71
0
)5sin(),5cos(i i
ii ioutputioutput
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Rotation Invariance (Cont.)
Router architecture Input is 20×20 window
of scaled image. Router has a single
hidden layer consistingof 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. Initializing F, a vector which will be the average position of each labeled feature over all the training faces.
2. Each face is aligned with F by computing rotation and scaling.
3. Transformation can be written as linear functions, we can solve it for the best alignment.
4. 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• •
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],,...,,...,,[ ,1,1,, WjWiijWjWiijijijWjWiijWjWiij xxxxxwxxxx
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Face Recognition Methods (Cont.) Hybrid NN
2. Self-Organizing Map
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Face Recognition Methods (Cont.) Hybrid NN
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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 NNsInvariant 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
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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.
Weights are updated after each pattern presentation.
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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
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Face Recognition Methods (Cont.) Hybrid NN
Substituting the SOM with the KLT
Replacing the CN with an MLP
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The tradeoff between rejection threshold and recognition accuracy
Face Recognition Methods (Cont.) Hybrid NN
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Face Recognition Methods (Cont.) Hybrid NN
Comparison with other known results on the same database
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Face Recognition Methods (Cont.) Hybrid NN
Variation of the number of training images per person
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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.