Facial Recognition System Using Local Binary

Patterns(LBP)

TS Vishnu Priya, G.Vinitha Sanchez, N.R.Raajan

School of Electrical & Electronics Engineering,

SASTRA Deemed University, Thanjavur, India

tsvpriya1995@gmail.com, gvinithasanchez2592@gmail.com, nrraajan@ece.sastra.edu

Abstract - There are several biometrics available like finger

print, iris identification etc. But Facial recognition or

detection is one of the biometric software applications that

can identify an particular individual in an digital image.

Face recognitions were used in many applications in the

field of banking, passport office etc. But the problem in the

face recognition is it cannot identify the person in the case

of identical twins. So the algorithm called local binary

patters were used to indentify the face in the case of

identical twins because the LBP can describe well about

the micro patterns present in the face.

Key Words - Micro Patterns, Pixels, Local Binary Patterns,

Histogram

I. INTRODUCTION Facial recognition is considered as a very tough challenge due

to variation in size, shape, color, and texture of human faces

and also there is no unique method to recognize the face

among the humans. Therefore in order to build a fully

automated system, a robust and efficient face recognition

method is required. The face recognition system consists of

recognizing the faces given as input with the data base

images[1]. There are several methods available to recognize

the face such as appearance based method, support vector

machine, hidden Markov model etc. This paper analysis a face

recognition based on local binary patterns which is

appearance based method.

II. EXISTING METHOD

In the existing system PCA method is used to recognize

the face[4]. Generally, PCA is used for reducing the dimension of the image. But one of the major problem with that is it cannot produce the complete information about the face therefore lose of information may occur in case of PCA algorithm. Also PCA algorithm cannot recognize face in case of identical twins.

III. PROPOSED METHOD

In order to overcome the above mentioned problems the

algorithm local binary pattern is proposed[2]. Since face

image is composed of several minute patterns this can be

efficiently identified by applying the local binary pattern

operator[5]. The local binary pattern operator is applied on the

given face image.

A. METHOD OF LOCAL BINARY PATTERNS(LBP):

In local binary pattern the input face is first converted into

the grey image and for that image the binary pattern is

calculated by comparing the center pixel with the surrounding

pixel.

Fig.1. Performance Of Local Binary Pattern (LBP) Operator

If the centre pixel is greater than that of the neighboring

pixel then it is denoted as 1 and if the neighboring pixel is

smaller than that of the centre pixel it is denoted as 0.This

should be done for each and every pixel so that we will get the

binary pattern.

Fig.2. Face image with pixels having uniform and non-uniform patterns

The local binary pattern is applied in the input image in

order to extract the important features of an image The

objective is to calculate the local binary pattern for each and

every pixels in an input image. Finally, the histogram is

calculated to find out the similarities of an given image.

In face recognition systems, the performance of the

algorithm is calculated by using the detection and false alarm

ratio .The common errors that occur in the face recognition

systems are,

False Negative: This error will occur because, the face is

not exactly recognized due to the poor ratio of detection

International Journal of Pure and Applied MathematicsVolume 119 No. 15 2018, 1895-1899ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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False Positive: This error will occur because, the non-face is

recognized as a face due to high ratio of false alarm.

The centre pixel co-ordinates are 𝑀𝑐 and 𝑁𝑐 ,then the co-

ordinates of the neighbor pixels are determined as follows,

𝑀𝑝 = 𝑅 cos 2 ∗ 3.14 ∗ 𝑝

𝑃 + 𝑀𝑐 (1)

𝑁𝑝 = 𝑅 sin 2 ∗ 3.14 ∗ 𝑝

𝑃 + 𝑁𝑐 (2)

𝑀𝑝𝑁𝑝 −Neighboring Pixels

𝑔𝑝 is the gray code of the neighbor pixels where p ranges

from 1 to (p-1), then the texture of an image can be calculated

as,

𝑇𝑥 = 𝑡 𝑔0 ,… . . 𝑔𝑝−1 (3)

Fig.3. Circular three different neighbor set for different values

Another possible way to find the texture of an image is by

subtracting the neighboring pixels values from the centre

pixels values which can given as follows, 𝑇𝑥 = 𝑡 𝑔0 − 𝑃𝑐 , … . . 𝑔𝑝−1 − 𝑃𝑐 (4)

𝑃𝑐 −Center Pixel

This means that that the neighboring(surrounding) pixel has

the high gray value when compared to the pixel at the center.

In that case the value is assigned as a one if not it is assigned

as a zero.

𝑇𝑥 = (𝑦 𝑔0 − 𝑃𝑐 , … . . 𝑦( 𝑔𝑝−1 − 𝑃𝑐)) (5)

𝑦 𝑛 = 0, 𝑛 < 01, 𝑛 ≥ 0

(6)

B. FEATURE VECTOR

After calculating the local Binary patterns for each and

every pixels the feature vector of the given image is

calculated. In order to get the efficient result the face is

divided into 𝑛2 region .In the below face image it is divided

into 𝑛2 i.e. 72 = 49 regions since n=7.

Fig.4. Face Image with n*n blocks

After dividing it into 𝑛2 region histogram is calculated for

each and individual pixel of an image, and then the histograms

for the region (𝑁𝑦 , 𝑁𝑧) can be calculated as,

𝐻𝐻𝑖 𝑁𝑦 , 𝑁𝑧 = 𝑋(𝐿𝐵𝑃 𝑦, 𝑧 = 𝐿 𝑖 )

𝑦 ,𝑧

(7)

𝑦 ∈

𝑅 + 1, … . .

𝑀

𝑘 ; 𝑁𝑦 = 1

𝑁𝑦 − 1 , … . . 𝑀 − 𝑅 ; 𝑁𝑦 = 𝑘

𝑁𝑦 − 1 , … . . 𝑁𝑦 𝑀

𝑘 ; 𝑒𝑙𝑠𝑒

(8)

𝑥 ∈

𝑅 + 1,… . .𝑁

𝑘 ; 𝑁𝑧 = 1

𝑁𝑧 − 1 , … . . 𝑀 − 𝑅 ; 𝑁𝑧 = 𝑘

𝑁𝑧 − 1 , … . . 𝑁𝑧 𝑁

𝑘 ; 𝑒𝑙𝑠𝑒

(9)

And finally, the histograms are concatenated in a single

vector feature.

yes

No

Fig.5. Block Diagram of Local Binary Pattern

Input Image

Face image is

divided into non

overlap blocks

Perform histogram

Histograms are

concatenated into

single feature

vector

More images?

Recognize

Face

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IV. STEPS TO BE PERFORMED FOR

FACE RECOGNITION There are the four important steps to be performed for face

recognition

1. FACE DETECTION:

This is the important initial step in the facial

recognition system, Performed to obtain pure facial images

with normalized intensity, uniform size and shape

2. FEATURE EXTRACTION:

Extracting the important Features in an face image is

done to obtain meaningful information that is useful to

identify the similarities between the different faces.

3.VERIFICATION:

The obtained Face image is then related with the

images available in the data base images. Once the obtained

image is matched with the data base image then it means that

the face is recognized otherwise it is not identified.

Table -1: Comparison of Various Methods and their performance

RESULTS AND DISCUSSIONS

INPUT IMAGE:

Here, the input image is loaded from the database and then it

is divided into 𝑛2 regions and local binary pattern is applied

for each region then histogram is calculated for each block

separately and finally it is concatenated into a single feature

vector.

OUTPUT IMAGE:

Here, the above output image will represent that the given

face image is recognized at the different directions

CONCLUSIONS

Since face image is composed of several minute patterns this

can be efficiently identified by applying the local binary

pattern operator. In this paper the more efficient facial

recognition technology is described that was successfully

applied to different analysis tasks, including face detection

and recognition, Iris detection, fingerprint recognition, and

problems related to expressions in face. This method will

extract the most important feature from the given image to

match the similarities between the different faces. Therefore

this local binary pattern method will work best when

compared to the other methods and also provides the efficient

result .

Methods Performance

Local Binary Pattern 89.3%

Principle Component

Analysis

64%

2D- Principle Component

Analysis

63.1%

Linear Discriminant

Analysis

55%

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Recognition Technique using Local Binary Pattern Method", International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 3, March 2015.

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[3] Srinivasulu Asadi , Dr.Ch.D.V.Subba Rao , V.Saikrishna, A Comparative study of Face Recognition with Principal Component Analysis and Cross-Correlation Technique, International Journal of Computer Applications (0975 – 8887),Vol.10,No.8, November 2010.

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[6] T. Ahonen, A. Hadid and M. Pietikainen, “Face description with Local Binary Patterns”, Application to Face Recognition. Machine Vision Group, University of Oulu, Finland, 2006.

[7] R. Gottumukkal and V.K. Asari, “An Improved Face Recognition Technique Based on Modular PCA Approach” Pattern Recognition Letters, vol. 25, pp. 429- 436, Mar. 2004.

[8] M. Kirby and L. Sirovich, “Application of the Karhunen-Loeve procedure for the characterization of human faces” IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(1):103{108}, 1990.

[9] W. Zhao and R. Chellappa “Robust face recognition using symmetric shape from-shading” Technical Report, Center for Automation Research, University of Maryland, 1999.

[10]R. Gottumukkal and V.K. Asari, “An Improved Face Recognition Technique Based on Modular PCA Approach” Pattern Recognition Letters, vol. 25, pp. 429- 436, Mar. 2004.

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