Comparison and Improvement of PCA and LBP Efficiency for Face Recognition

Rameez Qasim#1, M. Mutsaied Shirazi#2, Naveel Arshad#3, Ikram Qureshi#4, Sajjad Zaidi#5 # Pakistan Navy Engineering College, Karachi

National University of Science and Technology, Islamabad Pakistan 1rameez.qasim@ieee.org

5sajjadzaidi@pnec.edu.pk

Abstract- Face recognition for personal identification is a method which appealed researchers for many years. Many algorithms have been developed in this regard. Among many, two algorithms are selected for feasibility study. There real time accuracy is measured and compared. Some hardware arrangements are suggested for better accuracy making concept more feasible for real-time environment. Keywords— Principal Component Analysis, Local Binary Pattern, PCA, LBP, Comparison, Face Recognition, Face Detection

I. INTRODUCTION Human brain uses face recognition technique for

identification. Despite of huge changes in one’s face i.e., hair style, moustaches, beard, glasses, facial emotions or viewing condition, it is still able to recognize them. This ability of brain has fascinated philosophers and scientists for centuries. [1]

Among the most popular applications of digital image analysis by computer system is Face recognition. It is an identification technique in which an unknown person is identified by comparing his facial image with images of known in dataset. Ideally system returns the identity of person. This system not only has a tendency to replace current identification methods like passwords, ID cards and PIN codes but also exceptionally reliable methods of biometric person identification like retinal or iris scans and fingerprint. The drawback of methods just named is, they require attention of subject to perform identification whereas identification system based on image analysis doesn’t require subject’s attention or knowledge. Keeping in view that currently there are many commercial face recognition systems are in use it can be concluded that this way of identification still appeals researchers. [2]

II. RELATED WORK Face recognition is one of most significant application of

image analysis and image understanding. There are numerous industrial applications of face recognition, for instance video surveillance, photo cameras, human machine interaction, law enforcement, virtual reality and many more. The multidisciplinary nature of this subject attracts the researchers from diverse disciplines. But its relevance more lies in field of

pattern recognition, neural networks, computer graphics, image processing and psychology.[3]

In 1960’s researchers started focusing on face recognition. Panoramic Research, Inc. in Palo Alto, California first started research in this subject. This company worked majorly on Artificial Intelligence related contracts from U.S Department of Defense and various intelligence agencies. [4] In 1964 1965, Bledsoe, along with Helen Chand and Charles Bissonused computers for face recognition. [5, 6, 7, 8, 9] Researchers used the method of recognition is which subjective face features as ear size or between eye distance were measured. A. Jay Goldstein, Leon D. Harmon and Ann B. Lesk [10] used pattern classification techniques for recognition. They defined a vector consisting 21 subjective features like eye brow weight, nose length or ear protrusion. Fischler and Elschanger used similar method for recognition in 1973. [11] They used local template matching and a global measure of fit to measure and find face features.In 1985 Mark Nixon presented a geometric measurement for eye spacing. [12] He worked on improving the strategies of template matching by introducing “deformable Templates”.

III. THEORY The techniques for face recognition can be broadly

classified into following four categories.

1) Local Landmarks: Techniques of local land marks divide the image into various segments. Identify the required segments for recognition then algorithms are applied on them for information extraction and classification.

2) Holistic Image Analysis: In these techniques the one facial image is taken as one entity and algorithms are applied for information compression and classification.

3) Skin Texture Analysis: In these techniques the person’s skin texture is analysed.

4) 3 Dimensional Recognition: In 3D face recognition person’s three dimensional facial geometry is used. These systems have better accuracy then two dimensional recognition systems.

In this paper two algorithms are studied, one from Local landmark branch, Local Binary Patterns and other from Holistic branch, Principal Component Analysis.

978-1-4673-5885-9/13/$31.00 © 2013 IEEE

A. Principal Component Analysis Principal Component analysis (PCA) is a worthy method

for finding patterns in data with ability to express it in a way that similarities and differences are focused. As the dimensionality of data increases finding patterns in data become more difficult. PCA is a great tool for this purpose. [13] The main idea of PCA is to reduce dimensionality of dataset having numerous interrelated variables while keeping the maximum possible variations in dataset. [14]

B. Local Binary Pattern Local Binary Pattern (LBP) is a simple and very efficient

texture operator. It creates the binary pattern of every pixel of an image by thresholding the neighbourhood of each pixel and considers the result as a binary number. It has become a popular approach in face recognition because of its discriminative power and computational simplicity. The most important property of LBP operator in real-world applications is its robustness to monotonic gray scale changes. It is also computationally simple. This approach for feature extraction was introduced in 1996 by Ojala et al. [15]

IV. DATA SET Both the algorithms were tested initially on AT&T dataset,

which consists of 400 images of 40 individuals, 10 images per person. All the images are gray scaled, with size of 92x112 pixels [16]. The results were all satisfactory. The accuracy of both algorithms was 99%. But the concept is to measure accuracy of algorithms on real time captured images. For that reason we developed our own data of faces and divided into three sets. Dataset ‘A’ consists gray scale, 100x100 pixel, four images of four individuals, shown in Figure 1.

Figure 1: Dataset ‘A’

Dataset ‘B’ as shown in Figure 2 is further sub divided in four sets B1, B2, B3 and B4. All four contain 24 gray scaled images of 24 individuals. But image size is 50x50, 100x100, 200x200 and 300x300 respectively.

Dataset ‘C’ contains 48 gray scale images of 8 individuals. Six images of each, as shown in Figure 3.

After image capture, face is then detected, cropped, resized and converted to gray scale by using OpenCV, an open source computer vision library of image processing for C/C++. This library uses Viola-Jones algorithm to detect faces. [17]

Figure 2: Dataset ‘B’

Figure 3: Dataset ‘C’

V. ALGORITHM IMPLEMENTATION Both the algorithms were implemented on MATLAB. All

the experiments were done on already pre-processed images. In pre-processing, histogram equalization is also done via OpenCV.

A. Principal Component Analysis

The algorithm of PCA has been implemented in a very simple and hence efficient way. It is implemented in a series of steps. First all the training images are loaded in Matlab. Let’s say dataset A is loaded, which consists of four images of

size 100x100, as shown in Figure 2. Matlab treat images as 100x100 matrices. Then these matrices are converted to vectors of 1x10,000.

Γ

Γ

Γ

ΓThen average face is calculated by using following

formula:

Average face is shown in Figure 4.

Figure 4: Average Face.

Then difference faces are calculated by subtracting mean face from the original faces. So,

Φ Γ ΧΦ

Φ

Φ

Φ

Difference faces are shown in Figure 5.

Figure 5: Difference Faces

After that, covariance matrix is calculated from these difference images. Then Eigenvectors and Eigenvalues are then calculated from that covariance matrix. Eigenvectors are multiplied by difference faces to get Eigenfaces. These Eigenfaces are shown in Figure 6.

Figure 6: Eigenfaces

Now finally, a template is created by taking dot product of Eigenfaces and difference images. It contains all the information about all the four images. We call it ‘Register

Template’. Now when a new image comes, all the steps are repeated with that image, and in the end, instead of ‘Register Template’, ‘Login Template’ is calculated. Their correlation tells us whether the image is recognized or not. Later in this paper word confidence will be used instead of correlation.

B. Local Binary Pattern

In this algorithm, local binary pattern of each pixel is calculated by comparing the value of the central pixel to its neighbouring ones. If the value of neighbouring pixel is equal to or greater than the value of central pixel, it is taken as 1, otherwise 0. In this way a binary pattern is generated. As you can see in Figure 7.

Figure 7: Neighbouring pixels, LBP and decimal Value of LBP is shown of a pixel

The generated local binary pattern is categorized as uniform local binary pattern or non-uniform local binary pattern. A uniform local binary pattern is a binary pattern in which there is zero or two transitions from 0 to 1 or 1 to 0. One transition is not possible because we take local binary pattern as circular. A face image contains around more that 90% uniform local binary pattern, as shown in Figure 8. [18]

Figure 8: Face with only Uniform Local Binary Pattern pixels

Feature vector of every image from training set is calculated. To find a feature vector, an image is divided into 100 regions as shown in Figure 9. A histogram of each region is calculated, which defines the number of occurrence of uniform local binary patterns in that region as shown in Figure 10.

Figure 9: Face, divided into 100regions is shown

Every bin in Figure 10 defines the number of occurrence of that particular uniform local binary pattern. Since our LBP consist of 8 binary numbers, out of 59 patterns 58 are uniform patterns. Regional histogram of every region is then concatenated to form feature vector.

Figure 10: Histogram of one of the 100 regions

When a new image (model image) cvector is calculated. Then chi square staminimum distance estimation between newpreviously stored feature vectors (sample vin dataset. The least distance determines tonew image is it matched. Chi Square Sshown below:

Where S is the feature vector of samplthe feature vector of model image.

VI. RESULTS A. Principal Component Analysis

1) Image Resolution versus ConfidencB1, B2, B3 and B4. B2 performs the best 11.

Figure 11: Image Resolution versus Confidence

2) Image Resolution versus Timerecognition time increases as size of dashown in Figure 12 and Figure 13 respective

Figure 12: Image Resolution versus Learning Ti

comes, its feature atistic is used for

w feature vector and vectors) of images o which image the

Statistic formula is

le image, and M is

ce: Among datasets as shown in figure

e

e: Learning and ataset increases as ely.

ime

Figure 13: Image Resolution versu

3) Data Format versus Cdifferent formats i.e JPEG (jpgGraymap (pgm) was tested and It is observed that this is confidence.

4) Data Size versus Confidataset B2. Results are shown ias the number of images increlevel for same images decreases

Figure 14: Data Format versus Con

Figure 15: Data Size versus Confid

5) Data Size versus Timimages in dataset on learning tim

us Recognition Time

Confidence: Dataset A in three g), Bitmap (bmp) and Portable results are shown in Figure 14. no considerable change is

fidence: Images were used from n Figure 15. It is observed that

eases in dataset the confidence s for PCA.

nfidence

dence

me: The effect of number of me is shown in Figure 16.

Figure 16: Data Size versus Learning Time

The effect of number of images in datatime is shown in Figure 17.

Figure 17: Data Size versus Recognition Time

B. Local Binary Pattern

1) Data Size versus Distance: Imagewere used. Results are shown in Figure 1that size of dataset have no effect on distanc

Figure 18: Data Size versus Distance

2) Data Size versus Time: Learnintime increases as the size of dataset incrFigure 19 and 20 respectively.

aset on recognition

es from Dataset B2 8. It was observed ce between images.

ng and recognition rease, as shown in

Figure 19: Data Size versus Learni

Figure 20: Data Size versus Recog

C. PCA and LBP Data Size ver

Figure 21: Datasize versus Learnin

Figure 22: Data size versus Recogn

All the results shown ab

hardware arrangements as purpo

ing Time

gnition Time

rsus Time:

ng Time

nition Time

ove were calculated without osed later in this paper.

VII. RESULTS WITH HARDWARE ARRANGEMENTS Accuracy of PCA and LBP algorithm without booth, is

observed as 46% and 60% respectively. For practical purposes this accuracy is not satisfactory. It was observed that following factors were affecting accuracy

1. Uneven luminance on subject’s face. 2. Vertical camera alignment with subject’s face. 3. Distance of subject from camera.

For these reasons, a 58x34x80 inch booth was designed, in which motorize vertical moving camera assembly was installed. Camera is auto adjusted to nose position of subject. For constant luminance on subject’s face a 30W florescent tube serves the purpose. And a place was marked for persons to stand, to keep the distance from camera constant. Booth is shown in Figure 21. Booth was made such that there was an entrance and an exit.

Figure 23: A person standing inside Booth

It was observed that accuracy of algorithms was increased inside the booth. Results are shown is Table 1.

Without

Booth With Booth Recognition

Time (s) PCA 46% 65% 0.2247 LBP 60% 93% 5.2386

Table 1:Accuracy and recognition time of PCA and LBP with and without booth.

All the results are calculated on dataset ‘C’. The time is measure on Matlab on a Core 2 Duo processor, with windows 7 running on it. The time can further be decreased if a dedicated processor is used for it.

VIII. CONCLUSION The results conclude that Local Binary Pattern with

hardware arrangements give good performance in real time environment. Eliminating uneven lightening and camera misalignment increases accuracy to a great extent. Accuracy

of Principal component analysis is greatly affected with data size so it is not recommended.

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