Face Recognition Using Chain Codes

doi:10.4236/jsip.2013.43B027

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Journal of Signal and Information Processing, 2013, 4, 154-157

doi:10.4236/jsip.2013.43B027 Published Online August 2013 (http://www.scirp.org/journal/jsip)

Face Recognition Using Chain Codes

Nazmeen B. Boodoo-Jahangeer, Sunilduth Baichoo

Department of Computer Science, University of Mauritius, Reduit, Mauritius.

Email: [email protected]

Received May, 2013.

ABSTRACT

Face recognition is an active area of biometrics. This study investigates the use of Chain Codes as features for recogni-

tion purpose. Firstly a segmentation method, based on skin color model was applied, followed by contour detection,

then the chain codes of the contours were determined. The first difference of chain codes were calculated since the latter

is invariant to rotation. The features were calculated and stored in a matrix. Experiments were performed using the

University of Essex Face database, and results show a recognition rate of 95% with this method, when compared with

Principal Components Analysis (PCA) giving 87.5% recognition rate.

Keywords: Face Recognition; Biometrics; Chain Codes; PCA

1. Introduction

This study investigates the use of chain codes as features

for face recognition. Features representing images are

termed as descriptors. For robust recognition, descriptors

should not be sensitive to variations like: Size Change,

Translation and Rotation.

Common image descriptors include Area, Perimeter,

Compactness and Eccentricity. However these descrip-

tors are sensitive to variations. Such descriptors therefore

cannot be used as reliable features to represent objects.

Other object representations include Chain codes, Po-

lygonal Approximation, Signatures, Convex Hull, Fou-

rier Descriptor, Texture Content and Moments. The use

of chain codes have not been explored enough in litera-

ture.

In this work, the use of chain codes as descriptor has

been investigated. Face images have been pre-processed

to find the contours. The chain codes of the contours are

used as features for recognition.

2. Background Study

2.1 Face Recognition

Research in automatic face recognition dates back to the

1960’s [1]. A survey of face recognition techniques has

been given by Zhao, W. et al., [2]. In general, face rec-

ognition techniques can be divided into two groups based

on the face representation they use:

(a) Appearance-based, which uses holistic texture fea-

tures and is applied to either whole-face or specific re-

gions in a face image;

(b) Feature-based, which uses geometric facial features

(mouth, eyes, brows, cheeks etc.) and geometric rela-

tionships between them.

2.2. Chain Codes

Chain codes are one of the techniques that can recognize

characters and digits successfully. According to Seul et al

[3], this technique possesses several advantages. Firstly,

the chain codes are a compact representation of a binary

object. Secondly, it is easier to compare objects using

chain codes as they are a translation invariant representa-

tion of a binary object. Another advantage is that chain

code can be used to compute any shape feature as it is a

complete representation of an object or curve. Chain

codes provide lossless compression and preserve all

topological and morphological information which will be

useful in the analysis of line patterns in terms of speed

and effectiveness [4].

Jiang et al. (2009) made use of Chain codes to extract

features of fingerprint. The fingerprint image captured

was enhanced and binarized, the ridges of fingerprint

image were thinned, then freeman chain code was de-

rived to describe the extracted fingerprint ridge, match-

ing based on fingerprint ridge contour. This method has

proved to be the translation invariant. No results were

reported by the authors

3. Methodology

In this section, the database used, the pre-processing

techniques and the procedures followed are explained.

Face Recognition Using Chain Codes 155

3.1. Pre-Processing

Method 1: Canny Edge Detection

The original image is converted to grayscale and the

canny edge detection is applied to the image. Figure 1

shows the results.

(a)

(b)

Figure 1. (a) Original Image, (b) Image Edges.

Method 2: Skin Color Model

Several skin color detection techniques have been used

in literature [6,7]. A skin color model is applied for de-

tecting the face and extracting the background. The

method makes use of the YC rCb color space [8]. YCrCb

is an encoded nonlinear RGB signal, commonly used by

European television studios and for image compression

work. Color is represented by luma (which is luminance,

computed from nonlinear RGB, constructed as a

weighted sum of the RGB values, and two color differ-

ence values Cr and Cb that are formed by subtracting

luma from RGB red and blue components.

Y = 0.299R+0.587G+0.114B

Cr = R−Y

Cb = B−Y

This color space is suitable for skin color modeling

since it is simple to convert from RGB to YCrCb [9].

Face image segmentation using skin color model is

done by the following steps:

1) Read Original Image.

2) Convert image to YCrCb color space

3) Apply proper threshold to detect skin region in im-

age. Figure 2 shows the skin region detected.

4) Apply contour detection.

The resulting image was then used to detect the

boundaries in the face. The function retrieves contours

from the binary image and returns the number of re-

trieved contours. Figure 3 shows the result of boundary

detection where the face, the eyes and the nose bounda-

ries can be clearly seen. The largest boundary represents

the boundary of the face image and was considered for

further processing.

3.2. Algorithm

In this work, two algorithms have been studied, namely

the Principal Components Analysis (PCA), as described

by Turk and Pentland [10] and secondly the Chain

Codes.

Figure 2. Result of skin detection.

Figure 3. Result of Contour detection.

3.2.1. The PCA Procedure

Given an s-dimensional vector representation of each

face in a training set of M images, PCA tends to find a

t-dimensional subspace whose basis vectors correspond

to the maximum variance direction in the original image

space. This new subspace is normally low dimensional.

New basis vectors define a subspace of face images

called face space. All images of known faces are pro-

jected onto the face space to find sets of weights that

describe the contribution of each vector. To identify an

unknown image, that image is projected onto the face

space as well to obtain its set of weights. By comparing a

set of weights for the unknown face to sets of weights of

known faces, the face can be identified.

The main steps of PCA Algorithm are:

a) Determine PCA subspace from training data. ith im-

age vector containing N pixels is in the form

(1)

b) Store all p images in the image matrix

(2)

c) Compute covariance matrix

(3)

d) Compute eigenvalues and eigenvectors

(4)

where, is the vector of eigenvalues of the covariance

Face Recognition Using Chain Codes

156

matrix.

e) Order eigenvectors

(5)

f) Order the eigenvectors in according to their cor-

responding eigenvalues in descending order. Keep only

the eigenvectors associated with non-zero eigenvalues.

This matrix of eigenvectors forms the eigenspace ,

where each column of is the eigenvector. Visualized

eigenvectors of the covariance matrix are called eigen-

faces.

3.2.2. Chain C odes

Chain code is an object method representation based on

its boundary. The chain code of a boundary is determined

by specifying a starting pixel and the sequence of unit

vectors obtained from going either left, right, up, or

down in moving from pixel to pixel along the boundary.

The advantage of using chain codes is that it preserves

information and allows considerable data reduction. The

first approach for representing digital curves using chain

code was introduced by Freeman in 1961[11], and it is

known as Freeman Chain Code (FCC).

Figure 4. (a) 4 directional chain codes, (b) 8-directional

chain codes.

This code follows the contour in counter clockwise

manner and keeps track of the directions as we go from

one contour pixel to the next. This representation is

based on 4-connectivity or 8- connectivity of the seg-

ments [12]. The direction of each segment is coded by

using a numbering scheme as shown in Figure 4(a) and

Figure 4(b).

The first difference of a chain code is the number of

direction changes (in counterclockwise) between 2 adja-

cent elements of the code. This method is invariant to

rotation. Figure 5 shows the eight directions that a num-

ber in a chain code can follow.

The number of direction changes for each pair of pix-

els in the contour is determined. The feature vector is

created by counting the direction changes of each face’s

contours.

3.3. Database

The study used the Essex Face Database [13]. The train-

ing set included 400 images for 20 persons (20 images

per person). Sample faces are shown in Figure 1. Face

images were of 180 × 200 pixels. There were small

variations in facial expression, luminance, scale and

viewing angle and were shot at different time. Limited

side movement and tilt of the head were tolerated.

Figure 5. First Difference.

4. Results

Experiments done included testing chain codes with im-

ages on which canny edge detection and contour algo-

rithm were performed. Secondly, testing chain codes by

using the proposed skin color model followed by contour

detection was done. Both results were compared with

PCA algorithm, as shown in Table 1. The corresponding

False Acceptance Rate (FAR) and False Rejection Rate

(FRR) are also determined.

Table 1. Face Recgnition Results.

Recognition

Rate FAR FRR

PCA 87.5% 0.8 0.2

Chain codes using canny

edge detection and contour

detection

70% 0.4 0.1

Chain Codes using Skin

Model and contour detection95% 0.2 0.1

As shown in Table 1, the proposed system yields a

higher recognition rate when using the skin color model,

than when using canny edge detector. PCA outperforms

chain codes when the latter uses the canny edge detection.

This can be explained by the fact that the canny edges

contains a lot of noise which impacts performance,

This study shows that chain codes can be used as fea-

tures for face recognition. Further works include testing

this method for ear recognition and ultimately creating a

multi-model face and ear recognition system.

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