The document discusses the implementation of a face recognition system that enhances local features to overcome challenges posed by non-uniform motion blur and varying illumination conditions. It highlights the growing need for accurate user identification in electronic transactions and outlines the advantages of using face recognition technology, such as high accuracy and ease of use. The methodology involves utilizing the Local Binary Pattern (LBP) technique for improved face recognition accuracy and robustness against blur and illumination variations.

1. Under the guidance of Project
Mrs. J.SUSHMA By
K.GOPIKRISHNA
FACE RECOGNITION ACROSS
NON-UNIFORM MOTION BLUR AND ILLUMINATION

2. Contents
• Abstract
• Blur
• Illumination
• Face Recognition
• LBP Technique
• LBP Histogram
• Simulated Output
• Conclusion

3. Objective
• To implement face recognition system using
enhanced local features.

4. • The accuracy of the face recognition systems
degrades quite rapidly in unconstrained
settings.
• This arises from Blur and changes in
illumination.
ABSTRACT

5. Need of Face Recognition
• Everyday actions are increasingly being handled electronically,
instead of paper and pencil or face to face.
• This growth in electronic transactions results in great demand for
fast and accurate user identification and authentication.
• Access code for buildings, bank accounts and computer systems
often use PIN’s for identification and security clearances.
• Using the proper pin gains access, but the user of the PIN is not
verified. When credit or ATM cards are lost or stolen, an
unauthorised person can come up with the correct personal codes.
• Face recognition technology may solve this problem since a face is
undeniably connected to its owner except in the case of identical
twins.

6. Advantages of using face recognition
• It requires no physical interaction on behalf of the
user.
• It is accurate and allows for high enrolment and
verification rates.
• It can use your existing hardware infrastructure,
existing cameras and image capture devices, with no
problems.

7. Digital Image Processing
• The field of Digital image processing refers to
the processing digital images by means of a
digital computer.

8. Image
• An image may be
defined as a two-
dimensional function,
f(x, y), where x and y
are spatial (plane)
coordinates, and the
amplitude of f at any
pair of coordinates (x,
y) .

9. Types Of Digital Images
• Binary images
• Gray scale images
• Color images
• Indexed images

10. PIXEL
• Image element or PEL.
• In 8-bit gray scale image
the value of the pixel
range between 0 to
255.

12. SOFTWARE REQUIREMENT
MATLAB
• The MATLAB high-performance language for technical computing integrates
computation, visualization, and programming in an easy-to-use environment where
problems and solutions are expressed in familiar mathematical notation.
• Data Exploration ,Acquisition ,Analyzing &Visualization
• Application development programming using GUI building environment.
• Using MATLAB, you can solve technical computing problems faster than with
traditional programming languages, such as C, C++, and Fortran.

13. ADVANTAGES
• Very high accuracy.
• Verification time is generally less.
• Is the most economical.
• Easy to use.
• Small storage space required for the biometric template,
reducing the size of the database memory required.
• It is a cheap technology.

14. Practical implementation of some
image fundamental commands
Reading an image:
I = imread(‘i.tiff’);
Displaying an image:
imshow(I)
Resizing an image:
I = imread(‘1.tiff');
J = imresize(I, 0.5);
figure, imshow(I), figure, imshow(J)
Finding the size of an image:
[x y]=size(I)
[C D]=size(J)

15. BLUR
It is the apparent
streaking of rapidly
moving objects in a still
image.

16. What causes image Blur?
• Slow shutter speed
• Hand shake
• Misplaced focal point

17. ILLUMINATION
• Presence of light
intensity.
• Illumination range is
different during day
time and night time.

18. Face Recognition
In facial recognition there are two types of
comparisons:-
• Verification : The system compares the given
individual with who they say they are and gives a yes
or no decision.
• Identification : The system compares the given
individual to all the other individuals in the database
and give a ranked list of matches.

19. APPLICATION
• Face recognition is also useful in human computer interaction, virtual reality, database recovery, multimedia,
computer entertainment, information security e.g. operating system, medical records, online banking., Biometric
e.g. Personal Identification - Passports, driver licenses , Automated identity verification - border controls , Law
enforcement e.g. video surveillances , investigation , Personal Security - driver monitoring system, home video
surveillance system.
• Applications & examples: Face Identification: Face recognition systems identify people by their face images. Face
recognition systems establish the presence of an authorized person rather than just checking whether a valid
identification (ID) or key is being used or whether the user knows the secret personal identification numbers (Pins)
or passwords. The following are example.
• To eliminate duplicates in a nationwide voter registration system because there are cases where the same person was
assigned more than one identification number. The face recognition system directly compares the face images of the
voters and does not use ID numbers to differentiate one from the others. When the top two matched faces are highly
similar to the query face image, manual review is required to make sure they are indeed different persons so as to
eliminate duplicates.

20. Face Recognition Features
• Global features:
- Focus in the whole entire image
- Less accuracy
• Local features:
- Focus on the local features of the face which
help to identify and verify the persons using
the unique details in the face.
- More accuracy.

21. LBP(Local Binary Pattern)
• LBP works on the local features.
• LBP is defined as an ordered set of binary
comparisons of pixel intensities between the
centre pixel and its eight surrounding pixels.

22. Face recognition
local features Global features
LBP Gabor

23. LBP
• In a matrix originally of size 3x3,the values are
compared by the value of the centre pixel,
then binary pattern code is produced.
• The LBP code is obtained by converting the
binary code into decimal one.

24. LBP histogram
• Each pixel of an image is labelled with an LBP
code.
• First it will divide the image into several
blocks.
• Then it will start calculating the LBP histogram
for each block. After that it will combine every
LBP histogram for that image.
• Then we will get all the LBP histograms into
one vector.

25. LBP histogramLBP histogram

26. Conclusion
We proposed a methodology to perform face
recognition under the combined effects of non-
uniform blur and illumination using the LBP
technique and showed the closest match
image by comparing with all other images in
the database.

27. Simulated output for Blur

28. Simulated output for illumination

29. REFERENCES
• P. Vageeswaran, K. Mitra, and R. Chellappa, “Blur and illumination robust
face recognition via set-theoretic characterization,” IEEE Trans. Image
Process., vol. 22, no. 4, pp. 1362–1372, Apr. 2013.
• R. Gopalan, S. Taheri, P. Turaga, and R. Chellappa, “A blur-robust
descriptor with applications to face recognition,” IEEE Trans. Pattern Anal.
Mach. Intell., vol. 34, no. 6, pp. 1220–1226, Jun. 2012.
• V. M. Patel, T. Wu, S. Biswas, P. J. Phillips, and R. Chellappa, “Dictionary-
based face recognition under variable lighting and pose,” IEEE Trans. Inf.
Forensics Security, vol. 7, no. 3, pp. 954–965, Jun. 2012.
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