4th Student Conference on Research and Development (SCOReD 2006), Shah Alam, Selangor, MALAYSIA, 27-28 June, 2006

Human Face Recognition using Zernike

moments and Nearest Neighbor classifier

Nor’aini A. J.1, P. Raveendran1, N. Selvanathan2

1Department of Electrical Engineering, Faculty of Engineering University Malaya 50603, Kuala Lumpur 2Department of Artificial Intelligence, Faculty of Computer Science and Information Technology

University Malaya 50603, Kuala Lumpur, Malaysia

Abstract - This paper presents a human face recognition

system using an orthogonal invariant moment namely Zernike

moment (ZM) as a feature extractor and a simple Euclidean

square distance classifier or Nearest Neighbor. Changes in

illumination condition, pose, facial expression and others are

challenging task in recognizing face images. Solving these

problems requires a feature extractor that can generate

distinct features for each class of image and a classifier that

able to recognize and classify the face image precisely. The

experiments utilized database face images from Olivetti

research laboratory (ORL) consisting of 40 subjects of 10

images each where none of them are identical [1]. They vary

in position, rotation, scale, expression, with and without

glasses. The performance of the classification depends on the

moment order and classification error is observed below 10%.

Index terms - Euclidean square distance, orthogonal invariant moment, Zernike moments, nearest neighbor

I. INTRODUCTION Face recognition system is categorized as biometrics

similar to other recognition applications such as thumb print, hand-writing, iris scanning and many others. The used of moment invariant such as ZM moment in face recognition has long been research way back early nineties and now it is still being research although many of the latest work are either the improvement of existing techniques or hybrid techniques. Since it is orthogonal in nature and rotational invariant that makes it not sensitive to noise and has proven to be superior in terms of their feature representation capability [2]. The face recognition systems have wide range of application such as access control systems, content-based video browsing, building or office security, criminal identification and authentication in secure systems like computers or bank teller machines [3]. To build an automated system that can accomplish the above mentioned objectives is not an easy task. Constraints such as poses, illumination conditions, facial expressions, aging and many others are still the main problem to achieve high classification accuracy. However, a successful face recognition system depends heavily on the particular choice of feature extraction methods.

Irrespective of the method used, extracted features must minimize the within-class face variability and maximize between-class face variability in order to provide sufficient discrimination among different faces [3]. The application of ZM as feature extractor in face recognition

has interest several researchers such as A. Saradha et al, who combines Fourier descriptors with ZM and classifies the face images using Linear Discriminant Analysis (LDA) [4]. J. Haddadnia et al utilizes ZM on localized and segmented faces and classifies using Radial Basis function neural network [5], and N.H.Foon et al combines Wavelet Transforms (WT) and ZM as a feature vector using Euclidean distance as the classifier [6].

Besides that, other orthogonal moment based feature extraction methods such as, Lengendre moments, Pseudo Zernike moments, Krawtchouk moments and others have also gain attention lately. They have proven to be suitable for handling images with binary patterns such as pattern recognition, palm print verification and etceteras [7]. These moments acquire the characteristic of translation, scale and rotation invariance and thus can be chosen for image analysis and pattern recognition application [7]. Other feature extractors like PCA, Fourier descriptors and others have also gain attention and use as a hybrid feature to represent faces.

In this paper the classification results from the experiments utilizing feature vectors extracted from ZM with varying order is presented and analyzed. The data input consists of 40% train images and 60% test images. The rest of the paper is organized as follows. Section 2 describes the theory of ZM. Section 3 detailed out the classifier used for face recognition. Experimental results are presented in section 4. Section 5 analyses the performance of the feature extractors and finally section 6 concludes the paper.

II. ZERNIKE MOMENTS The kernel of Zernike moments are orthogonal

Zernike polynomials defined over the polar coordinates inside a unit circle. The Zernike moment of order p is defined as [].

!

!ddrrrfrV

pZ pqpq ),(),(

)1( 2

0

1

0" "

#$% (5)

where, Vpq(r, ) denote Zernike polynomials of order p and repetition q and is written as

jqer

pqRr

pqV )(),( % (6)

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4th Student Conference on Research and Development (SCOReD 2006), Shah Alam, Selangor, MALAYSIA, 27-28 June, 2006

while # denotes complex conjugate. The radial polynomial, Rpq(r) is expressed as

& '

spr

qspqsps

spA

Axsqp

spq

R

2

!2

2!

2

2!

)!(

12/

0

(

))*

+,,-

. (())*

+,,-

. $(

(%

(/(

%%

)*+,

-.

(7)

where p is a non-negative integer, and q is an integer such

that qp ( is even, and pq 0 . The discrete

approximation of the continuous Zernike integral of equation (1) is written as follows

10

),,(

)(),(1

0

1

0

^

11

2% / /

(

%

(

%

(

ij

N

i

N

j

qj

ijpqpq

rfor

jif

erRNpZij

3 (8)

where, 3(p,N) is the normalizing constant based on the mapping transformation [8]. Zernike moment is used as the feature extractor whereby the order is varied to achieve the optimal classification performance.

III. NEAREST NEIGHBOR CLASSIFIER Nearest-neighbor classification method has a very

wide application and is regard as the simplest and powerful method. It is used to compare the feature vector of the prototype image and the feature vectors stored in the database. This is obtained by calculating the Euclidean square distance between the prototype image and the database. For instance let C1, C2, C3 … Ck be the K clusters in the database. The class is found by measuring the distance d(x(q), k) between x(q) and the K th cluster Ck.The feature vector with minimum difference is found to be the nearest matching vector [4]. The expression for the minimum distance is given by

4 5kq

kq CxxxCxd 6(% :min),( )()( (9)

In the experiments, the square distance is computed to determine the minimum difference that best match the feature vector of the stored database.

IV. EXPERIMENTAL RESULTS The experiments were conducted on various numbers

of subjects from ORL database of 40 subjects where each subject consists of 10 different orientations of the images. Each image is of size 92x112 and to reduce the complexity in computation, the images are resized to 64x64. Figure 1 shows the sample subject in ORL database with different orientation used in the experiments. They are selected at

random where 4 from each subject are considered as train images and the other 6 as test images.

Fig. 1: Some of the face images from ORL database

Table 1(a) and 1(b) show the various order considered in the experiments based on 5 subjects and 20 subjects respectively. The train images are image 1 to image 4.

TABLE 1(a) % PERFORMANCE OF VARIOUS ORDERS (5 SUBJECTS)

order No. of features

Class. accuracy (%)

825

83.33

1036

83.33

1249

90.00

1464

96.67

1681

96.67

2 to 8 23

93.33

2 to 10 34

93.33

2 to12 47

100.00

2 to 14 62

100.00

2 to 16 79

100.00

TABLE 1(b) % PERFORMANCE OF VARIOUS ORDERS (20 SUBJECTS)

OrderNo. of features

Class. accuracy (%)

2 to 8 23

88.33

2 to 10 34

89.17

2 to 12 47

91.67

2 to 14 62

91.67

2 to 16 79

91.67

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4th Student Conference on Research and Development (SCOReD 2006), Shah Alam, Selangor, MALAYSIA, 27-28 June, 2006

Table 2 and 3 listed some of the feature descriptors of the sample image shown in Figure 1 that include moment order 0 and 1 and exclude moment order 0 and 1 respectively.

TABLE 2 ZM DESCRIPTORS OF SAMPLE IMAGE

(INCLUDE MOMENT ORDER 0 AND 1)

TABLE 3 ZM DESCRIPTORS OF SAMPLE IMAGE (EXCLUDE MOMENT ORDER 0 AND 1)

The selection of the order at which ZM performs the best is experimented by considering first, all the moments starting from 0 and then omitting moments 0 and 1. It is observed

that classification accuracy is better when moments 0 and 1 are disregard. Moments of orders 2 to 10 and 2 to 12 have shown somewhat optimal performance as in Table 1(a) and 1(b). Based on these results, the rest of the experiments as the number of subjects is increased only consider ZM of order 2 to 10 and 2 to 12 for comparison in terms of percentage classification accuracy. Table 4 and Table 5 show the results obtained from the experiments considering order 2 to 10 and 2 to 12 respectively. The number of subjects considered in these experiments is 5, 20, 25, 30 and 40. Experiments conducted on 5 to 25 subjects do not include those wearing glasses while 30 to 40 include those with glasses but they are all subjected to constraints as mentioned earlier. The experiments are not restricted to one set of train images but the train images are picked at random as tabulated in Table 4 and 5 respectively. The number of subjects wearing glasses in the experiment of 30 subjects is 5 and increased to 15 when 40 subjects are considered.

V. PERFORMANCE ANALYSIS Table 1(a) shows the performance of ZM when

moments 0 and 1 are considered and disregard respectively. The classification accuracy is very much bias to ZM with moments 0 and 1 omitted. The reason being moments at lower order are basically noise and this may creates confusion in face recognition.

Comparing the results obtained in Table 4 and 5, the best performance of 5 subjects is similar for both orders 2 to 10 and 2 to 12 that is 100%. At 20 subjects, order 2 to 10 has overall classification accuracy at 94.17 % while order 2 to 12 is at 96.67% that is 2.5% higher. When 25 subjects are considered, classification accuracy of 94.67% at order 2 to 10 and 96% at order 2 to 12 are achieved. This shows that the performance of order 2 to 12 is 1.33 % higher than order 2 to 10. 5 subjects with glasses are included in the experiment with 30 subjects. The best classification accuracy obtained for order 2 to 10 and 2 to 12 are 94.44% and 96.11% respectively. Order 2 to 12 outperforms by 1.67%. The number of subjects with glasses is increased to 15 at 40 subjects for both orders. Order 2 to 10 shows 90.83% classification accuracy while order 2 to 12 shows 92.5% classification accuracy that is 1.67% higher. However, for both orders the classification error is below 10%.

Looking at the results above, it is pretty obvious that order 2 to 12 performs better compared to order 2 to 10. It is also observed that the higher the order of moments the higher the classification accuracy since higher order moments confine better and has more detail information about the face image [6].

VI. CONCLUSION Orders 2 to 10 and 2 to 12 of ZM possess the best

features that optimally illustrate the face. The selection of which order to consider that can give the best overall classification accuracy is very subjective since it is very

Moment Order Feature img1_c5 img2_c5 img3_c5 img4_c5

0,0 1 0.568217 0.589011 0.622875 0.513356

1,0 2 0.951478 0.913442 0.737145 0.969321

2,2 3 0.322923 0.460294 0.479715 0.366224

2,0 4 0.124056 0.210402 0.26807 0.073281

3,3 5 0.678247 0.706593 0.647946 0.752306

3,1 6 0.621506 0.583958 0.472364 0.627314

4,4 7 0.972441 0.94563 0.905653 0.946786

4,2 8 0.153475 0.229023 0.271778 0.132799

4,0 9 0.369332 0.265661 0.22445 0.354366

5,5 10 0.721863 0.750831 0.72899 0.695351

5,3 11 0.476047 0.539621 0.454635 0.580448

5,1 12 0.313247 0.374939 0.317518 0.262082

6,6 13 0.481687 0.545315 0.4615 0.480398

6,4 14 0.853394 0.821301 0.798999 0.799079

6,2 15 0.608804 0.248813 0.322386 0.658696

6,0 16 0.566112 0.602494 0.572533 0.537671

Moment Order Feature img1_c5 img2_c5 img3_c5 img4_c5

2,2 1 0.322923 0.460294 0.479715 0.366224

2,0 2 0.124056 0.210402 0.26807 0.073281

3,3 3 0.678247 0.706593 0.647946 0.752306

3,1 4 0.621506 0.583958 0.472364 0.627314

4,4 5 0.972441 0.94563 0.905653 0.946786

4,2 6 0.153475 0.229023 0.271778 0.132799

4,0 7 0.369332 0.265661 0.22445 0.354366

5,5 8 0.721863 0.750831 0.72899 0.695351

5,3 9 0.476047 0.539621 0.454635 0.580448

5,1 10 0.313247 0.374939 0.317518 0.262082

6,6 11 0.481687 0.545315 0.4615 0.480398

6,4 12 0.853394 0.821301 0.798999 0.799079

6,2 13 0.608804 0.248813 0.322386 0.658696

6,0 14 0.566112 0.602494 0.572533 0.537671

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4th Student Conference on Research and Development (SCOReD 2006), Shah Alam, Selangor, MALAYSIA, 27-28 June, 2006

dependent on the choice of database that is either the database has less constraints or otherwise, the combination techniques employed and choice of the classifiers. In

general, the performance of ZM as a feature extractor requires a supporting technique that able to improve the overall performance of the face recognition system [4-6].

TABLE 4 % PERFORMANCE AT ORDER 2 TO 10

Class. accuracy(%)

Train images image1-image4 image5-image8 image9,10,1,2 image3_image6 image7-image10

No of subjects

5(50images)w/o glass 93.33 100.00 100.00 100.00 100.00

20(200images)w/o glass 89.17 94.17 92.50 87.50 91.67

25(250images)w/o glass 87.33 94.67 90.00 87.33 90.67

30(300images) w/glass 87.78 94.44 91.67 88.33 91.11

40(400images)w/glass 80.83 90.83 88.75 84.17 83.75

TABLE 5 % PERFORMANCE AT ORDER 2 TO 12

Class. accuracy (%)

Train images image1-image4 image5-image8 image9,10,1,2 image3_image6 image7-image10

No of subjects

5(50images)w/o glass 100.00 100.00 100.00 100.00 100.00

20(200images)w/o glass 91.67 95.83 95.83 90.83 96.67

25(250images)w/o glass 89.33 94.67 96.00 90.67 94.67

30(300images) w/glass 89.44 95.00 96.11 90.00 94.44

40(400images)w/glass 84.17 91.25 92.50 87.08 83.75

This can be done either looking in terms of preprocessing of the images before extracting the features using ZM or combining other feature extraction methods. However the idea of how to select the order at which ZM performs best is presented and this helps to make consideration which order that can be utilized effectively for further processing and thus developing an effective face recognition system. Lots of factors need to be studied to achieve high classification accuracy and able to develop a robust face recognition system.

VII. REFERENCES [1] Olivetti Research Laboratory,

http://www.uk.research.att.com:pub/data/att_faces.zip[2] Belkassim S. O, “Pattern Recognition with moment invariants-

Acomparative study and new results”, Pattern Recognition, Vol. 24 No. 12(1991), pp1117-1138.

[3] Javad Haddadnia et-al, “Neural Network Based face Recognition with moment invariants, IEEE transaction, 2001.

[4] A. Saradha, S. Annandurai, “A Hybrid Feature Extraction Approach for Face Recognition

Systems”, International Journal on Graphics, Vision and Image Processing, V5, 2005.

[5] J. Haddadnia, K. Faez, “Human Face Recognition with Moments Invariant”, Proceeding on NSIP 2001, Conference NSIP 2001

6] N.H.Foon et al “An efficient Method for Human Face Recognition using Wavelet Transform and Zernike Moments”, Proceedings of the InternationalConference on Computer Graphics, Imaging and Visualization (CGIV’04).

[7] Y.C.Chin, A.A.Kassim, Y.Ibrahim,“Character recognition using statistical moments”, Image Vision computing, Vol. 17, pp 299-307, 1999.

[8] R. Mukundan, K.K.Ramakrishnan, “Fast computation of Legendre and Zernike moments”, Pattern Recognition, Vol. 28, No. 9, pp1433-1442, 1995.

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