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Kailash J.Karande & Sanjay N Talbar

International Journal of Image Processing (IJIP) Volume (3) : Issue (3) 120

Independent Component Analysis of Edge Information

for Face Recognition

Kailash J. Karande kailashkarande@yahoo.co.in

Department of Information Technology,

Sinhgad Institute of Technology, Lonavala,

Dist-Pune, Maharashtra State. (India) 410401.

Sanjay N. Talbar sntalbar@yahoo.com

Department of Electronics & Telecommunication

SGGS Institute of Engineering &Technology, Nanded,

Maharashtra State, India.

Abstract

In this paper we address the problem of face recognition using edge information

as independent components. The edge information is obtained by using

Laplacian of Gaussian (LoG) and Canny edge detection methods then

preprocessing is done by using Principle Component analysis (PCA) before

applying the Independent Component Analysis (ICA) algorithm for training of

images. The independent components obtained by ICA algorithm are used as

feature vectors for classification. The Euclidean distance and Mahalanobis

distance classifiers are used for testing of images. The algorithm is tested on two

different databases of face images for variation in illumination and facial poses

up to 1800rotation angle.

Keywords:

Principle Component analysis (PCA), Independent Component Analysis (ICA), Laplacian of

Gaussian ( LoG) and Canny edge detection Euclidean distance classifier, Mahalanobis distance classifier.

1. INTRODUCTION

Face recognition is a task that humans perform routinely and effortlessly in their daily

lives. Wide availability of powerful and low-cost desktop and embedded computing

systems has created an enormous interest in automatic processing of digital images and

videos in a number of applications, including biometric authentication, surveillance,

human-computer interaction, and multimedia management. Research and development

in automatic face recognition follows naturally.

Research in face recognition is motivated not only by the fundamental challenges this

recognition problem poses but also by numerous practical applications where human

identification is needed. Face recognition, as one of the primary biometric technologies,

became more and more important owing to rapid advances in technologies such as

digital cameras, the Internet and mobile devices, and increased demands on security.

Face recognition has several advantages over other biometric technologies: It is natural,

non intrusive, and easy to use. Among the six biometric attributes considered by

Hietmeyer [1], facial features scored the highest compatibility in a Machine Readable

Travel Documents (MRTD) [2] system based on a number of evaluation factors, such as

enrollment, renewal, machine requirements, and public perception.

Kailash J.Karande & Sanjay N Talbar

International Journal of Image Processing (IJIP) Volume (3) : Issue (3) 121

A face recognition system is expected to identify faces present in images and videos

automatically. It can operate in either or both of two modes: (1) face verification (or

authentication), and (2) face identification (or recognition). Face verification involves a

one-to-one match that compares a query face image against a template face image

whose identity is being claimed. Face identification involves one-to-many matches that

compare a query face image against all the template images in the database to

determine the identity of the query face.

There are two predominant approaches to the face recognition problem: geometric

(feature based) and photometric (view based). As a researcher interest in face

recognition continued and many different methods are proposed, like well studied

methods using Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA)

and Elastic Bunch Graph Matching (EBGM). In order to organize the vast field of face

recognition, several approaches are conceivable. For instance, algorithms treating the

face and its environment as uncontrolled systems could be distinguished from systems

that control the lighting or background of the scene, or the orientation of the face. Or

systems that use one or more still images for the recognition task could be distinguished

from others that base their efforts on video sequences.

The comparison of face recognition using PCA and ICA on FERET database with

different classifiers [3] [4] are discussed and found that the ICA has better recognition

rate as compared with PCA with statistically independent basis images and also with

statistically independent coefficients. Their findings are based on the frontal face image

datasets are encouraging with few face expressions. Marian S Bartlett used version of

ICA [5] derived from the principle of optimal information transfer through sigmoidal

neurons on face images from FERET database has proved that ICA representation gave

the best performance on the frontal face images. Feature selection in the independent

component subspace [6] which gives the benefits for face recognition with changes in

illumination and facial expressions. Fusion of ICA features like Spatial, Temporal and

Localized features [7] [8] for Face Recognition are considered as optimization method.

An independent Gabor features (IGFs) method and its application to face recognition

using ICA [9] is discussed by Chengjun Liu; Wechsler, H. The novelty of the IGF method

comes from 1) the derivation of independent Gabor features in the feature extraction

stage and 2) the development of an IGF features-based probabilistic reasoning model

(PRM) classification method in the pattern recognition stage. In particular, the IGF

method first derives a Gabor feature vector from a set of down sampled Gabor wavelet

representations of face images, then reduces the dimensionality of the vector by means

of principal component analysis, and finally defines the independent Gabor features

based on the independent component analysis (ICA).

The illumination has great influence on how a face image looks. Researchers have

proved that for a face image, the difference caused by illumination changes has even

exceeded the difference caused by identity changes [10]. The big challenge of face

recognition lies in dealing with variations of pose, illumination, and expression. Also there

is need to address the problem of identity changes using structural components.

In this paper we propose the face recognition using Edge information and ICA with large

rotation angles with poses and variation in illumination conditions. Here we used the edge

information of face images using different standard edge detection function operators like

canny, Laplacian of Gaussian (log). The edge information is being used to calculate

independent components for face recognition. We used the database which has the large

rotation angles up to 180

change between the images of person while looking right and

or left. We considered the face images having various orientations of the face i.e.: looking

front, looking left, looking right, looking up, looking up towards left, looking up towards

right, looking down. In this study we considered the samples of individual person which

Kailash J.Karande & Sanjay N Talbar

International Journal of Image Processing (IJIP) Volume (3) : Issue (3) 122

consist of sufficient number of images having expressions, changes in illumination and

large rotation angles. For illumination variation the effects of light on face image from left,

right, top and bottom sides are considered. The paper is organized as follows. In Section

2 we introduce the ICA and information about edge detection in section 3. The section 4

specifies the need of the preprocessing before applying the ICA algorithm. The modified

Fast ICA algorithm is presented in the Section 5 and classifiers are discussed in section

6. Experimental results are discussed in Section 7 and accordingly the conclusions are

drawn in Section 8.

2. Introduction to ICA

ICA will be rigorously defined as a statistical ‘latent variable’ model. Assume that we

observe n linear mixtures x

, ....., x

of n independent components

njnjjj

sasasax +++= ......

2211

For all j. (1)

we have now dropped the time index t; in the ICA model, we assume that each mixture x

as well as each independent component s

is a random variable, instead of a proper time

signal. The observed values x

(t), e.g. the microphone signals in the cocktail party

problem, are then a sample of this random variable. Without loss of generality, we can

assume that both the mixture variables and the independent components have zero

mean. If this is not true, then the observable variables x

can always be centered by

subtracting the sample mean, which makes the model zero-mean.

It is convenient to use vector-matrix notation instead of the sums like in the previous

equation. Let us denote by x the random vector whose elements are the mixtures x

, ...,

. And likewise by s the random vector with elements s

, ..., s

. Let us denote by A the

matrix with elements a

. Generally, bold lower case letters indicate vectors and bold

upper-case letters denote matrices. All vectors are understood as column vectors; thus x

or the transpose of x, is a row vector. Using this vector-matrix notation, the above mixing

model is written as

x =As (2)

Sometimes we need the columns of matrix A; denoting them by a

the model can also be

written as,

x =

∑

i 1

(3)

The statistical model in equation.(2) is called independent component analysis or ICA

model[11]. The ICA model is a generative model which means that it describes how the

observed data are generated by a process of mixing the components s

. The independent

components are latent variables, meaning that they cannot be directly observed. Also the

mixing matrix is assumed to be unknown. All we observe is the random vector x, and we

must estimate both A and s using it. This must be done under as general assumptions as

possible.

The starting point for ICA is the very simple assumption that the components s

are

statistically independent. It will be seen that we also assume that the independent

component must have nongaussian distributions. However, in the basic model we do not

assume these distributions known (if they are known, the problem is considerably

simplified). For simplicity, we are also assuming that the unknown mixing matrix is

square, but this assumption can be sometimes relaxed. Then, after estimating the matrix

A, we can compute its inverse, say W, and obtain the independent component simply by:

s = Wx. (4)

Kailash J.Karande & Sanjay N Talbar

International Journal of Image Processing (IJIP) Volume (3) : Issue (3) 123

ICA is very closely related to the method called blind source separation (BSS) or blind

signal separation. A “source” means here an original signal, i.e. independent component,

like the speaker in a cocktail party problem. “Blind” means that we know very little, if

anything, on the mixing matrix, and make little assumptions on the source signals. ICA is

one method perhaps the most widely used, for performing blind source separation.

3. Edge Detection

Edges are boundaries between different textures. Edge also can be defined as

discontinuities in image intensity from one pixel to another. The edges for an image are

always the important characteristics that offer an indication for a higher frequency.

Detection of edges for an image may help for image segmentation, data compression, and

also help for well matching, such as image reconstruction and so on.

Laplacian of a Gaussian (LoG) Detector: Consider the Gaussian function

)(

erh

−

−=

(5)

where r

and σ is the standard deviation. This is smoothing function which, if

convolved with an image, will blur it. The degree of blurring is determined by the value of σ.

The Laplacian function [13] is

)(

−













−

−=∇

(6)

For obvious reason, this function is called the Laplacian of a Gaussian (LoG). Because of

the second derivative is a linear operation, convolving an image with

)(

rh∇

is the same

as convolving the image with the smoothing function first and then computing the Laplacian

of the result. This is the key concept underlying the LoG detector.

Canny Edge Detection:

Finds edge by looking for local maxima of the gradient of f(x, y). The gradient is calculated

using the derivative of a Gaussian filter. The method uses two thresholds to detect strong

and weak edges and includes the weak edges at the output only if they are connected to

strong edges. Therefore this method is more likely to detect true weak edges. The Canny

edge detector[13] is the most powerful edge detector provided by function edge. In this

method the image is smoothed using a Gaussian filter with a specified standard deviation,

σ, to reduce noise. The local gradient,

[

]

2/1

),( yGxGyxg +=

(7)

and edge direction,

)/(tan),(

GxGyyx

−

(8)

are computed at each point. Gx and Gy computed using sobel, prewitt or Roberts method

of edge detection. An edge point is defined to be a point whose strength is locally maximum

in the direction of the gradient.

4. Preprocessing by PCA

There are several approaches for the estimation of the independent component analysis

(ICA) model. In particular, several algorithms were proposed for the estimation of the basic

version of the ICA model, which has a square mixing matrix and no noise. Practically when

applying the ICA algorithms to real data, some practical considerations arise and need to

be taken into account. To overcome these practical considerations we implemented a

preprocessing technique in this algorithm that is dimension reduction by principal

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