Image Registration and Nose Detection Using Affine ... Registration and Nose Detection Using Affine Transformation ... mathematical concepts and the ... features that can form a good

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Image Registration and Nose Detection Using Affine Transformation

C.Victoria Priscilla,

Assistant Professor,

Information Technology

S.D.N.B.Vaishnav College For

women, Chennai.

B.Poorna,

Professor and Principal,

SSS Shasun College for Women,

Chennai

Abstract

Automated face recognition face the difficulties in the

human pose, face expression, lighting conditions,

orientation, etc. To overcome the problem to the some

extent, the proposed method works with the simple mathematical concepts and the transformation

concepts which are applied with morphological

operations to achieve the invariant recognition.

Automatic face recognition usually normalizes the face

images as the preprocessing step and then proceeds

with the recognition. In the proposed method image

registration is implemented using affine

transformation, morphological operations and

mathematical theory (trigonometric) to normalize the

face which is invariant to transformations.

Keywords: Registration point, transformations,

Morphological processing, Mathematical concepts,

feature Extraction, Normalization.

1. Introduction

Face recognition has attracted significant attention

because of its wide range of applications [1, 2]. Pattern

recognition [3, 7] has been an important area in

computer vision applications. In the case of a planar

image, there are four basic forms of geometric

distortion caused by the change in camera location: translation, rotation, scaling and skew. All of these can

be represented by the affine transformation [7]. A

tensor-based moment function method has been

developed to recognize objects under distortion of

translation, rotation, scaling and skew [3, 4]. By the

tensor theory [3], they derived a simple equation from

which an angle can be calculated to make the pattern

invariant to rotation. An important face feature point is

the nose tip. This is because the nose is the highest

protruding point from the face. Besides that, it is not

affected by facial expressions. Another important

function of the nose is that it is able to indicate the

head pose. Thus, the resulting pattern is invariant to

translation, rotation, scaling and skew.

1.1 Image registration

Image registration [5] is the process of overlaying two

or more images of the same scene taken at different

times, from different viewpoints, and/or by different

sensors. It geometrically aligns two images the

reference and sensed images. The present differences

between images are introduced due to different

imaging conditions. The majority of the registration methods consist of the following four steps:

i) Feature detection. Salient and distinctive objects

(closed-boundary regions, edges, contours, line

intersections, corners, etc.) are manually or, preferably,

automatically detected. For further processing, these

features can be represented by their point

representatives (centers of gravity, line endings,

distinctive points), which are called control points

(CPs) in the literature.

ii) Feature matching. In this step, the correspondence

between the features detected in the sensed image and

those detected in the reference image is established.

Various feature descriptors and similarity measures

along with spatial relationships among the features are used for that purpose.

iii) Transform model estimation. The type and

parameters of the so-called mapping functions,

aligning the sensed image with the reference image, are

estimated. The parameters of the mapping functions

are computed by means of the established feature

correspondence.

iv) Image resampling and transformation. The

sensed image is transformed by means of the mapping

functions. Image values in non-integer coordinates are

computed by the appropriate interpolation technique.

Registration methods [5] can be categorized with

respect to various criteria. The ones usually used are

C Victoria Priscilla et al, Int.J.Computer Technology & Applications,Vol 4 (2),209-216

IJCTA | Mar-Apr 2013 Available [email protected]

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the application area, dimensionality of data, type and

complexity of assumed image deformations,

computational cost, and the essential ideas of the

registration algorithm. Here the registration method is

based on the essential ideas of the registration

algorithm based on feature detection method in order

to recognize the face invariant to transformation.

1.2 Image Transformations

In image processing, normalization is a process that

changes the range of pixel intensity values.

Recognition of objects and patterns [3] that are

deformed in various ways has been a goal of much

recent research. In normalization approach, the objects

are transformed into some standard position before they are classified. In geometry, a transformation is a

process by which a set of points is transformed, or

changed. These changes can involve location, size, or

both. The fundamental objective of 2D transformation

[9] is to simulate the movement and manipulation of

objects in the plane. Points and lines which join them

along with appropriate drawing algorithm are used to

represent objects. The ability to transform these points

and lines is achieved by translation, rotating, scaling

and reflection.

1.2.1. Translation

To represent affine transformations with matrices, we can use homogeneous coordinates. This means

representing a 2-vector (x, y) as a 3-vector (x, y, 1), and

similarly for higher dimensions. Using this system,

translation can be expressed with matrix multiplication.

The functional form ;

becomes:

1.2.2. Scaling

For scaling (that is, enlarging or shrinking), we have

and . The matrix form is:

When , then the matrix is a squeeze mapping and preserves areas in the plane.

1.2.3. Rotation

For rotation by an angle θ counter clockwise about the

origin, the functional form is

and .

Written in matrix form, this becomes:

Similarly, for a rotation clockwise about the origin, the

functional form is and

and the matrix form

is:

1.3 Mathematical Morphology

Image processing techniques [8] have been

tremendously developed during the past five decades,

and among them, mathematical morphology has been

continuously receiving a great deal of attention. It is

because mathematical morphology provides

quantitative description of geometric structure and

shape, as well as mathematical description of algebra,

topology, probability, and integral geometry.

Mathematical morphology has been proved to be extremely useful in many image processing and

analysis applications. The word morphology refers to

any scientific study of form and structure. This term

has been widely used in biology, linguistics, and

geography. In image processing, a well-known general

approach is provided by mathematical morphology,

where the images being analyzed are considered as sets

of points and the set theory is applied on the

morphological operations. This approach is based upon

logical relations between pixels, rather than arithmetic

relations, and can extract geometric features by

choosing a suitable structuring shape as a probe.

1.4. Feature Extraction

Feature extraction (or detection) [8] aims to locate

significant feature regions on images depending on

their intrinsic characteristics and applications. These

regions can be defined in global or local

neighbourhood and distinguished by shapes, textures,

sizes, intensities, statistical properties, and so on. Local

feature extraction methods are divided into intensity

based and structure based. Intensity-based methods

analyze local intensity patterns to find regions that

satisfy desired uniqueness or stability criteria.



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Structure-based methods detect image structures such

as edges, lines, corners, circles, ellipses, and so on.

Feature extraction tends to identify the characteristic

features that can form a good representation of the

object, so as to discriminate across the object category

with tolerance of variations. Facial feature extraction

[10] has become an important issue in automatic

recognition of human faces. Detecting the basic feature

as eyes, nose and mouth exactly is necessary for most face recognition method and chosen the operator

SUSAN (Smallest Univalue Segment Assimilating

Nucleus) to extract the edge and corner points of local

feature area. Hua Gu et al.[10] approach is

automatically locate the feature points with high

accuracy as for most front face images of luminance,

even for some small angle left and right rotation face

images, but it is still limited in the application of large

angle rotation with reducing of the accuracy and is

partly impacted by strong sidelight.

2. Proposed Method for Image Registration

Face recognition method is used in identifying or

verifying one or more persons of interest in a scene by

comparing input faces with face images stored in a

database. Abundance of methods is in existence for

feature extraction. In the proposed method, for any query image a registration point, tip of the nose is used

for normalization as the algorithm given below. The

idea is to find the tip of the nose on the face; the nose is

normally close to the center of the face. Pixels that are

closer to the centroid should be extracted using the

morphological operations.

Using this we can able to find out the largest connected component in the vertical direction. So the largest

connected component in the region which is closest to

the centroid is the tip of the nose. If the pixels lie

within the region is classified as nose. Once the tip of

the nose is detected, the other two points of the left and

the right eyes are detected. Based on these the center

points of the eyes the midpoint is determined. These

points are important to find the angle of rotation as

shown in the algorithm and the figure 2. This method

uses the affine transformation, morphological

processing and the mathematical theory to transform

the image into standard form so that the normalized

image used to extract feature irrespective of the

position. Mostly, the eyes are located first, then the

nose, mouth etc. The interesting part of the algorithm is

detecting the tip of the nose. Based on this position,

other landmarks are located for the recognition to apply the mathematical approach. The following figure

Figure 1. shows the steps for normalization before

proceed for face recognition.

Figure 1. Steps for normalization for face recognition



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3. Algorithm

1. Translate the coordinate to the image centroid for the

given image. Shift the position to the desired location.

2. Scale the image to the fixed size of square dimensions

to change the size of an object as in Figure 1.

3. With the centroid find the closest region using

morphological processing to find the tip of the nose. The largest connected component which is closest to the

centroid is the point which is the tip of the nose. Fix that

point as the registration point (0, 0) as O.

4. Based on the tip of the nose, eyes are detected. From the

tip of the nose, move upwards towards the y axis and find

both the left and the right side of the region to find eye

points. Using morphological and filtering operation detect

the center point of the eye in the darker region. Fix that left

side of the point as L and the right side of the point as R.

5. Find the midpoint M using the L and R which lies in the line towards the point O as in Fig 2b.

6. If the point M is on the vertical straight line towards the

positive Y axis and the point R is on the quadrant Q1, point

L is on the quadrant Q2 then the image is straight towards

900.

7. If the point M is on the horizontal straight line towards

the negative X axis and the point R is on the quadrant Q2,

point L is on the quadrant Q3 then the image is on the 1800.

Similarly it applies the same for 2700

and 3600.

8. If the point M is on the left diagonal line towards the

positive Y axis and the point R is on the upper portion of

the quadrant Q2, point L is on the lower portion of the

quadrant Q2 then the image is slanting towards 450

as in

Figure 2c. This is same for all the diagonal lines.

9. If the point M lies in the somewhere else near the diagonal line or near the vertical or horizontal lines in any

quadrant say Q2, point L is on the lower portion of the Q2

then the image lies in between 0 to 450

as in Figure 2d.

Here the angle of rotation is to be determined for the

triangle OMM‘.

10. Similarly angle of rotation can be determined for different angles as shown in Table 2.

a). Square dimensions to fix the image.



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b). Image which looks straight towards 900 c). Image which looks slanting diagonally left

d). Image slanting towards 450

e) Angle of rotation. in the anticlockwise.

Figure 2. Image to determine the angle of rotation of various angle.

Angle of rotation is the measure of degrees that a figure is

rotated about a fixed point. We can use the Pythagorean

theorem and properties of quadrant sines, cosines, and

tangents to solve the triangle, that is, to find unknown parts

in terms of known parts. Using the formulae in (2.1) we

can able to find the angle of distance from 900 and rotate

towards to that angle so that image looks straight towards

900.

Pythagorean theorem: a2 + b

2 = c

2. --. (1.1)

Sines: sin A = a/c, sin B = b/c. --. (1.2)

Cosines: cos A = b/c, cos B = a/c. -- (1.3)

4. Experimental Results

The experiment in this paper is based on the Indian face

database which is available online www.face-rec.org/. The

Indian face database is used for the implementation which

randomly selects the frontal faces with different poses and

expressions. But the images contains only the frontal faces

has been taken for the training and test images. Only the

frontal faces of 300 images have been modified using the

Adobe Photoshop for the slanting faces.

The algorithm is evaluated and tested with the Indian face

database with 300 faces (males and females) each of size

640 X 480 pixels. The images rotated with various angles

and tested to find the angle of rotation. Detecting the tip of

the nose and the fixing the tip of the nose to point (0,0) is the difficult task. Once the region of the nose is identified it

can be used to find the angle of rotation as per algorithm.

The angle of rotation is determined using the trigonometric

theory to solve the triangle, which is to find the unknown

parts in terms of known parts. The system can able to find

out the angle of rotation irrespective of the large rotation

variation. The nose will always lie at the center of the face,



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the nose is detected correctly when the face looks straight

towards 900. The proposed method is able to determine

detection rate and the false positive rate. Detection rate is

calculated for all the faces irrespective of their orientation.

False positive rate is occurred in some images only when

the angle has been changed from 900. False positive

reduces red when the image is straight. We can calculate

the precision which is the fraction of retrieved documents

that are relevant to the search:

Precision = TP / TP + FP

Table 1: Evaluation results based on nose tip detection

.

Table 2. Angle of rotation for various angles

a) input face b) Detected nose region

No. Of Images Nose detected

correctly

True Positive in

%

False positive in

%

Precision rate in %

300 284 94.666 0.6667 94.666

Images Left eye

(L)

Right eye (R) Midpoint

(M)

Registration

point (O)

Point (M’) Angle of

rotation

imrot1.jpg (-74,26) (-3,-84) (-39,-29) 0,0 0,-29 53.3659

imrot2.jpg (25,225) (-100,165) (-38,195) 0,0 0,195 101.0271

imrot3.jpg (-94,26) 20,-86 -37,-30 0,0 0,30 50.9645

imrot4.jpg (98,30) -20,225 39,128 0,0 0,128 196.9453

imrot5.jpg 3,-84 74,26 39,-29 0,0 0,-29 323.3659



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c) tip of the nose (0,0) d) Points to detect angle of rotation

e) Normalized face Fig 4. Finding angle of rotation for rotated face

a) input face b) Detected nose region



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c) tip of the nose (0,0) d) Normalized face

Fig 5. face lies towards 900

.

5. Conclusion

In this paper the proposed method is used for fixing the

registration point, detecting the nose invariant to

transformation. Since the normalization step is needed

before the face recognition, the proposed method is

implemented and tested invariant to transformations.

The normalization algorithm is easy and does need not

much computation. Locating the landmarks in the face

is not an easy task in the feature extraction method.

Hence the accurate landmarks are needed to find the

matching face in the automatic system. The proposed

method is fully automated and invariant to large

rotation which is large enough to recognize the face. In

future work the proposed method can be extended to conduct experimental studies using other various

publicly available face databases.

6. References

[1]. R. Chellappa, C. L.Wilson, and S. Sirohey, ―Human

and machine recognition of faces: a survey,‖ Proc. IEEE, vol. 83, no. 5, pp. 705–740, May 1995

[2]. Weilong Chen, Meng Joo Er, Member, IEEE, and

Shiqian Wu, Member, IEEE, Illumination

Compensation and Normalization for Robust Face

Recognition Using Discrete Cosine Transform in

Logarithm Domain, IEEE TRANSACTIONS ON

SYSTEMS, MAN, AND CYBERNETICS—PART B:

CYBERNETICS, VOL. 36, NO. 2, APRIL 2006

[3]. Soo-Chang Pei and Chao-Nan Lin, Image

normalization for pattern recognition image and Vision

Computing Volume 13 Number 10 December 1995

711, 1995 Elsevier Science B.V.

[4]. Cyganski, D and Orr, J A, ‗Applications of tensor theory to object recognition and orientation

determination‘, IEEE Trans. PAMI, Vo17 No 6

(November 1985) pp 662-673.

[5]. Barbara Zitova, Jan Flusser ,Image registration

methods: a survey, Image and Vision Computing 21

(2003) 977–1000

[6]. Jan Flusser, ―Moment Invariants in Image Analysis‖,

World Academy of Science, Engineering and

Technology 11 2005

[7]. K. Lam and H. Yan, ―Fast Algorithm for Locating

Head Boundaries,‖ J. Electronic Imaging, vol. 3, no. 4,

pp. 351-359, 1994.

[8]. Frank. Y. Shih, Image processing and pattern recognition- fundamentals and techniques, IEEE Press,

Copyright 2010 by the Institute of Electrical and

Electronics Engineers, Inc. Published by John Wiley &

Sons, Inc., Hoboken, New Jersey.

[9]. Computer Graphics, ISRD group, TataMcGraw-Hill,

2006.

[10]. Hua Gu Guangda Su Cheng Du, Feature Points

Extraction from Faces, Image and Vision Computing

NZ, Palmerston North, November 2003,pp 154-158

[11]. Wei Jen Chew, Kah Phooi Seng, and Li-Minn Ang, Nose Tip Detection on a Three-Dimensional Face

Range Image Invariant to Head Pose, Proceedings of

the International MultiConference of Engineers and

Computer Scientists 2009 Vol I IMECS 2009, March

18 - 20, 2009, Hong Kong.



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Image Registration and Nose Detection Using Affine ... Registration and Nose Detection Using Affine Transformation ... mathematical concepts and the ... features that can form a good