Work Completion Seminar - Abstract
Umi Sabriah bt Haron @ Saharon
A Modified SPCA for Face Recognition with
One Training Image Sample per Person
INTRODUCTION
Face recognition is one of the few biometric systems which have the advantages of retaining
high accuracy and low intrusiveness, while being a user-friendly method at the same time. It is
also a personal identification system based on analysis of frontal or profile images of the face,
without the participant’s cooperation or knowledge. Due to its numerous advantages, face
recognition technology can be relevant to various fields especially in the wide range of law
enforcement and surveillance application, information security, smart cards and access control
systems. Face recognition can be defined as a technique which identifies or verifies one or more
persons in a scene by comparing input faces with face images stored in a database [16].
For the past 20 years, both the academic and industrial communities have paid more attention
and interest on the research and development of face recognition technologies. Various surveys
and evaluations on different aspects of face recognition technologies were conducted by several
authors [9]. Generally, face recognition can be classified into three categories, i.e., holistic,
feature-based and hybrid method. The most widely used algorithms for the holistic method are
eigenface [11], Fisherfaces [15], and ICA [1], while the most often used technique in the second
category includes elastic bunch graph matching [13], hidden Markov model [6], and pure
geometry methods [3]. The eigenface [11] technique concept is to find the principal components
of the distribution of faces, or the eigenvectors of the covariance matrix of the set of face images.
The eigenvector contains the features which together characterize the variation between face
images.
Among the various face recognition methodologies, the holistic-based approaches seem to be the
most successful [12] with the methods such as eigenface and fisherfaces dominating the
literature. However, when it come to realistic face recognition applications, holistic-based
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Work Completion Seminar - Abstract
Umi Sabriah bt Haron @ Saharon
methodologies often suffer from the so-called small sample size problem, where the number of
training samples is much smaller than the dimensionality of the image space [12].
RESEARCH MOTIVATION
Even after many years of research, there are still many challenges in the field of face recognition.
Researchers have focused and proposed several methodologies to address the problems of
illumination, variation in pose, and expression by using geometrical features, local features,
neural networks, elastic bunch graph matching, and wavelets [9]. However, only a few
researchers [4,9,12,14,17] have focused on the challenges of face recognition when only minimal
training images of an individual are available.
Generally, face recognition systems use at least three good quality face images of every
individual captured in a controlled environment. However, these idealistic conditions do not exist
for many law enforcement and surveillance applications, forensic identification and access
control systems. These real-world applications are facing the limitation in retrieving and
collecting large face samples and would usually have only one training sample per person in the
database. The one training sample per person problem can be viewed as an extreme small sample
size problem, which seriously provokes the traditional face recognition applications.
The performance of numerous holistic methods is heavily affected by the number of training
sample for each face. Fisherfaces [15] and support vector machine (SVM) [7] for examples,
claim to be superior in the performance compared to other approaches, but when there is only
one training image per person, their performance degrade tremendously or sometimes even fail
to work [10]. The reason behind this situation is because most face recognition techniques
assume that there are several (at least two) samples of the same person has been collected and
stored in the database. Therefore, the significance and challenges of the real-world application
which commonly obtain only one sample of training image per person triggers the rapid
emergence of active research to confront this crucial challenge.
Furthermore, the study of this specific issue is also vital as there are further advantages of storing
only one sample per person, whereby the task of collecting samples becomes much easier, as
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Work Completion Seminar - Abstract
Umi Sabriah bt Haron @ Saharon
well as reducing the storage and computational cost. Various techniques had been initiated to
address the problem of one sample per person, such as synthesizing virtual samples, localizing
the single training images, probabilistic matching and neural network methods [10].
This study aims to address the one sample per person problem by implementing holistic-based
methods, in specific, employing the extension of principal component analysis technique and
enlarging the size of the training set. Research work by Wu and Zhou, namely a method called
(PC)2A [14] was studied. This method enriched the information of face space. The (PC)2A
method was extended by Chen et al. later with a method named E(PC)2A [2], which generalized
and further enhanced the previous method. The essence of both (PC)2A and E(PC)2A istrying to
enrich the information of eigenspace by perturbing spatially the single training sample [10].
Another similar method studied was the singular vector decomposition (SVD) perturbation [17]
introduced by Zhang et al. This method, named SPCA, became the basis for the research work
which we conducted.
This work includes introducing two new methods to tackle the issue of one sample per person,
and experiments were conducted to compare the performance of the methods introduced with the
other methods of similar approaches, based on the number of training images per person and
gallery size. Furthermore, operations of the new methods were evaluated based on several
performance measure techniques.
RESEARCH OBJECTIVES
1. To examine and study the different holistic face recognition methods proposed by other
researchers to address the single training sample per person problem.
2. To propose a holistic face recognition method based on SVD concept to manage the one
training image sample per person issue.
3. To propose a holistic face recognition method based on SVD concept and image filtering
to manage the one training image sample per person issue.
4. To evaluate the robustness and performance of the proposed methods with other methods
of similar approach.
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Work Completion Seminar - Abstract
Umi Sabriah bt Haron @ Saharon
THESIS OUTLINE

Chapter 1 is the Introduction. It briefly discusses the issues on face recognition. It further
describes the research methodology, motivation, scope and objective of the study.
Finally, it gives the outline of the thesis.

Chapter 2 is the Literature Review. It discusses on the previous works in the related field
followed by the contributions of this study.

Chapter 3 explains the general background of the implemented methodologies.
This chapter is divided into four main section:
o Overview on principal component analysis (PCA) and eigenface methods.
o Overview on singular value decomposition (SVD) concept.
o Overview on image filtering techniques.
o Overview on performance measure techniques, which are Euclidean distance,
City Block distance, Simplified Mahalanobis distance and Cosine distance.

Chapter 4 discusses on the two face recognition methods proposed to address the single
sample problem which are the Modified SPCA I and Modified SPCA II.

Chapter 5 elaborates on the experimentation conducted to evaluate the performance of
Modified SPCA I and Modified SPCA II in comparison with other methods of similar
approach, and also discusses the analysis of the experimentation results.

Chapter 6 includes some conclusion about the work done and the future perspectives of
the research
RESEARCH CONTRIBUTION
The contribution of this study can be summarized as follows:

In Chapter 4, we presented a new face recognition algorithm, Modified SPCA I by
applying the concept of SVD.

Furthermore, in Chapter 4, we introduced Modified SPCA II by employing the concept of
SVD and integrated with the image filtering concept.

In Chapter 5, we include a much broader comparison of the two proposed methods with
other methods of similar approaches. Among the evaluation, we reported how the
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Work Completion Seminar - Abstract
Umi Sabriah bt Haron @ Saharon
algorithms performance is influenced by the number of training images per person and by
gradually increasing the gallery image size. Besides that, we also determined the best
performance measure technique for the single sample problem.
REFERENCES
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Umi Sabriah bt Haron @ Saharon
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