The document summarizes research on using an extreme learning machine with local receptive fields (ELM-LRF) for face recognition. ELM-LRF was tested on three face datasets: Caltech, achieving 98.15% accuracy; CBCL, achieving 98.34% accuracy; and UFI, achieving 66.11% accuracy. ELM-LRF has advantages over other methods like reduced training time, fast results, no getting stuck in local minima, and finding the best weights in one iteration due to local connections between input and output. The research concludes that ELM-LRF is well-suited for face recognition systems.

1. Firat University
Graduate school of natural
and applied science
Local Receptive Fields Based Extreme Learning
Machine For Face Recognition
By
Aras M.Ismael
Supervised by
Asst. Prof. Abdulkadir ŞENGÜR
15 February 2018

2. Outline
• Introduction
• Brief introduction to the Humans Brain
• How humans brain work
• Biological inspiration
• Artificial Neural network
• Neural network modules
• Type of Neurons
• Face recognition algorithms
• Extreme learning machine
• Testing
• Conclusion

3. Introduction
As the necessity for higher levels of security rises, technology is bound
to swell to fulfill these needs. Any new creation, enterprise, or
development should be uncomplicated and acceptable for end users in
order to spread worldwide. This strong demand for user-friendly
systems which can secure our assets and protect our privacy without
losing our identity in a sea of numbers.

4. Introduction (cont.)
Biometrics is the emerging area of bioengineering; it is the automated
method of recognizing person based on a physiological or behavioral
characteristic. There exist several biometric systems such as signature,
finger prints, voice…etc

5. Brief introduction to Humans Brain
• The knowledge concerning how a human brain works perfectly has
remained unknown over time.
• However, much is known about information is processed. The
function of neurons is to gather signals emanating from other
neurons through dendrites

6. How humans brain work

7. Biological inspiration
synapses
axon
dendrites
 The information transmission happens at the synapses.
 electrochemical stimulation received from other neural cells to the
cell body by dendrites.

8. Biological inspiration
 The spikes travelling along the axon of the pre-synaptic neuron trigger the release of
neurotransmitter substances at the synapse.
 The neurotransmitters cause excitation or inhibition in the dendrite of the post-synaptic
neuron.
 The integration of the excitatory and inhibitory signals may produce spikes in the post-
synaptic neuron.
 The contribution of the signals depends on the strength of the synaptic connection.

9. Artificial neurons
Neurons work by processing information. They receive and provide information in form of
spikes.

10. Neural networks

11. Neural network modules(cont.)
Multi layer Neural network

12. Back propagation

13. Back propagation problem(cont.)
 Delta(learning rule): which it implements weight changes and The learning rule in a multilayer
perceptron is not guaranteed to produce convergence, and it is possible for the network to fall into a
situation (the so called local minima) in which it is unable to learn the correct output.
 Isolation

14. Applications of neural networks
Character Recognition - The idea of character recognition has become
very important as handheld devices like the Palm Pilot are becoming
increasingly popular. Neural networks can be used to recognize
handwritten characters.
• Image Compression - Neural networks can receive and process vast
amounts of information at once, making them useful in image
compression. With the Internet explosion and more sites using more
images on their sites, using neural networks for image compression is
worth a look.

15. Applications of neural networks(cont.)
• Stock Market Prediction - The day-to-day business of the stock
market is extremely complicated.
• Medicine, Electronic Nose, Security, and Loan Applications.
• Health usage
• Face recognition applications

16. Neural network in face recognition

17. Local Receptive Fields Based Extreme Learning
Machine For Face Recognition

18. Extreme learning machine
• Extreme learning machines are feedforward neural
network for classification, regression, clustering, sparse
approximation, compression and feature learning with a single layer or
multi layers of hidden nodes, where the parameters of hidden nodes
(not just the weights connecting inputs to hidden nodes) need not be
tuned.

19. Extreme learning machine(cont)
• The name "extreme learning machine" (ELM) was given to such
models by its main inventor Guang-Bin Huang.
• these models are able to produce good generalization performance
and learn thousands of times faster than networks trained
using backpropagation.

20. Extreme learning machine (cont.)
• Unlike traditional learning methods, such as BP algorithm, ELM does
not require any iterative tunings. It presents better accuracy and high
efficiency, in various applications such as system modelling,
biomedical analysis, power systems, etc.

21. Local respective fields
A hidden node can be a sub-network of several nodes, which turn out
to form local receptive fields and (linear or nonlinear) pooling.

22. ELM-LRF results in the following face datasets
• Caltech face dataset
• Cbcl face dataset
• UFI face dataset

23. Caltech face dataset
The dataset has 10,524 human appearances of different resolutions
and in various settings
The Caltech confront dataset has 450 images of 27 people. Every ha
diverse helping qualities and a size of 64×64 pixels.

24. Caltech face dataset(cont.)
• Based on ELM-LRF testing accuracy in the given dataset can see that
ELM-LRF has more advantage over face recognition compare to other
methods test in this dataset.
Method Testing Accuracy (%)
NR MODEL 32.36
SCSPM 82.83
TSR 37.45
SPARSE BASED NN 92.91
ELM-LRF 98.15

25. CBCL face dataset
The training set comprises of 6,977 images (2,429 faces and 4,548
nonfaces), and the test set comprises of 24,045 images (472
countenances and 23,573 nonfaces).
 The MIT-CBCL dataset contains 2,000 face pictures. The calculation
utilized 150 images for training (15 images for each class with right,
left, and frontal perspectives).

26. CBCL face dataset(cont.)
Methods Testing Accuracy (%)
ELM-LRF 98.34
Pose invariant 95.40
Original C2 features [14] 87.05
MPCALDA [15] 88.53

27. UFI dataset
This dataset contain images of 605 people.
The images are cropped to a size of 128 x 128 pixels.

28. UFI face dataset(cont.)
• In testing accuracy by using UFI dataset can be seen that the testing
accuracy of the proposed ELM-LRF method is an enhancement on
LBPHS is 55.44 %, LDPHS, and FS-LBP, although it achieved the face
size also significantly differs and the faces are not localized after that
it slightly lower test accuracy than that of POEMHS which reduced
12.06% difference.
Method Testing Accuracy (%)
LBPHS 55.04
LDPHS 50.25
POEMHS 67.11
FS-LBP 63.31
ELM-LRF 66.11

29. Result of ELM-LRF

30. Conclusion
Reduced training time
Fast result
No isolation in this method
The outcome can be found in one iteration

31. Conclusion (cont.)
based on our result by using ELM-LRF in face recognition the
proposed method can have more advantage in face recognition
systems because it will set the best weight for the given input
Because the input has local connection with the desired output, it
can set the best weight for the given output.
Based on our result , we can say that the ELM-LRF can pass back-
propagation problems which it can not find its weight easily.