Under the guidance of Project Mrs. J.SUSHMA By

K.GOPIKRISHNA

FACE RECOGNITION ACROSS NON-UNIFORM MOTION BLUR AND ILLUMINATION

Contents

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

Objective

• To implement face recognition system using enhanced local features.

• The accuracy of the face recognition systems

degrades quite rapidly in unconstrained settings.

• This arises from Blur and changes in illumination.

ABSTRACT

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.

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.

Digital Image Processing

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

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) .

Types Of Digital Images

• Binary images• Gray scale images• Color images• Indexed images

PIXEL

• Image element or PEL.• In 8-bit gray scale image

the value of the pixel range between 0 to 255.

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.

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.

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)

BLUR

It is the apparent

streaking of rapidly moving objects in a still image.

What causes image Blur?

• Slow shutter speed• Hand shake• Misplaced focal point

ILLUMINATION

• Presence of light intensity.

• Illumination range is different during day time and night time.

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.

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.

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.

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.

Face recognition

local features Global features

LBP Gabor

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.

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.

LBP histogramLBP histogram

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.

Simulated output for Blur

Simulated output for illumination

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.

• D. Yi, Z. Lei, and S. Z. Li, “Towards pose robust face recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2013, pp. 3539–3545.

• ] S. Cho, Y. Matsushita, and S. Lee, “Removing non-uniform motion blur from images,” in Proc. Int. Conf. Comput. Vis., Oct. 2007, pp. 1–8.

Thank You