Neural Network based Supervised Self Organizing Maps for Face Recognition

International Journal on Soft Computing (IJSC) Vol.3, No.3, August 2012
DOI: 10.5121/ijsc.2012.3303 31
NEURAL NETWORK BASED SUPERVISED SELF
ORGANIZING MAPS FOR FACE RECOGNITION
A.S.Raja1
and V. JosephRaj2
1
Research Scholar, Sathyabama University, Jeppiar Nagar, Chennai,Tamil Nadu, India
csehod@capeitech.org,
2
Professor, Kamaraj College, Thoothukudi, Tamil Nadu, India
v.jose08@gmail.com
ABSTRACT
The word biometrics refers to the use of physiological or biological characteristics of human to recognize
and verify the identity of an individual. Face is one of the human biometrics for passive identification with
uniqueness and stability. In this manuscript we present a new face based biometric system based on neural
networks supervised self organizing maps (SOM). We name our method named SOM-F. We show that the
proposed SOM-F method improves the performance and robustness of recognition. We apply the proposed
method to a variety of datasets and show the results.
KEYWORDS
Biometrics, Face, Supervised Self Organizing Maps (SOM).
1. INTRODUCTION
The increase of terrorism and other kinds of criminal actions, such as fraud in e-commerce,
increased the interest for more powerful and reliable ways to recognize the identity of a person [1,
2]. To this end, the use of behavioural or physiological characteristics, called biometrics, is
proposed. Biometrics is best defined as measurable physiological and or behavioural
characteristics that can be utilized to verify the identity of an individual [1]. Many physiological
characteristics of humans, i.e., biometrics, are typically invariant over time, easy to acquire, and
unique to each individual. Therefore the biometrics traits are increasingly adopted for civilian
applications and no longer confined for forensic identification.
The recognition of individuals without their full co-operation is in high demand by security and
intelligence agencies requiring a robust person identification system. Many face recognition
algorithms have been proposed so far [3, 4, 5, 6, 7, 8]. Algorithms related to recognition of face,
hand geometry, iris, voice recognition have also been proposed (See Handbook of Biometrics
[9]). It is estimated that 5% of the population does not have legible fingerprints [1], a voice could
be altered by a cold and face recognition systems are susceptible to changes in ambient light and
the pose of the subject.
A typical biometric system usually consists of that specific biometric detection scheme followed
by an extraction methodology (which shrinks the dimensionality of useful information) and then a
classifier to make the appropriate decision. There are linear approaches such as principal
component analysis (PCA) or Eigen faces method [10], independent component analysis (ICA)

32
[11,12], and linear discriminant analysis (LDA) [13,14] available in literature. Besides there are
several nonlinear manifold analysis approaches that used the Kernel method, such as kernel
principal component analysis (KPCA) and kernel linear discriminant analysis [15,16, 17]. A
survey of methods and the comparative results from literature are presented below in Table 1.
Previous
works
Feature extraction
method
Database Correct recognition rate
[18]
FSS or Eigenfaces
method
Yale face database and
constructed a particular
database
FSS is varying from 77.8 to
95.3%
Eigenface is varying from 65.6
to 76%
[19]
Eigenfaces method,
ICA, or LDA
FERET data set Varying from 64.94 to 83.85%
[20]
Edge information +
traditional PCA + ICA
Indian face database and
Asian face database
Varying from 74 to 95%
[21]
Kernel discriminant
analysis
FERET, ORL and GT
databases
Error recognition rate varying
from 43 to 1.5%
[22]
Eigenfaces or
fisherfaces
FERET dataset
Error recognition rates ranging
from 35 to 1.5%
[23]
Eigenfaces, MLP as a
feature extractor, Or
SOM network
MIT face database Ranging from 72.4 to 83.07%
[Present
Paper]
Neural Network Based
Supervised Self
Organizing Maps For
Face Recognition
IIT Delhi Database Ranging from 88.25% to 98.3%
In this paper, we provide a new approach for appearance-based face based recognition that does
not require a subject’s cooperation called as SOM-F method. We use neural network based
supervised self organizing maps for dimensionality reduction as a part of SOM-F method. We
show that our proposed SOM-F method improves the performance and robustness of recognition
when compared to methods proposed in literature. SOM-F stands for Supervised self organizing
maps (SOM) for Face.
The remainder of this paper is organized as follows: In section 2 we discuss an object
identification technique suitable for face. In section 3 we explain how SOM is used with Face for
recognition. In section 4 we discuss the results obtained using this SOM-F method. Paper
concludes with conclusion and future direction.
2. OBJECT DETECTION
We extract the regions of interest using a Haar like features based object detector provided by the
open source project OpenCV library [24]. This form of detection system is based on the detection
of features that display information about a certain object class to be detected. Haar like features
encode the oriented regions whenever they are found, they are calculated similarly to the
coefficients in Haar wavelet transformations. These features can be used to detect objects, in this
case the human face. The Haar like object detector was originally proposed by Viola and Jones
[25] and later extended by Lienhart and Maydt [26].
We used the dataset related to face retrieved from [27] for our experiments. To create the face
detector we also used 1000 positive samples and 2500 negative images. The positive images were
scaled to a size of 25x25 to reflect the rectangular dimensions of the face. The face detector

33
worked well with a few falsely detected faces, the problem was overcome by selecting the larger
detected object.
Fig. 1.1 Example raw images from the dataset of IIT Delhi [27]
Fig. 1.2 Example images from the dataset of IIT Delhi [27] after face detection.
3. SOM & METHODOLOGY USED IN THIS PAPER
Self Organizing Map (SOM) is a special kind of unsupervised computational neural network [28]
that combines both data projection (reduction of the number of attributes or dimensions of the
data vectors) and quantization or clustering (reduction of the number of input vectors) of the input
space without loss of useful information and the preservation of topological relationships in the
output space.
A few concepts are useful to understand the workings of the technique. The input space (also
called signal) is the set of input data we employ to feed the algorithm; the set of input data in our
case refers to the set of images that we use for training; typically, the observations are
multidimensional and are thus expressed by using a vector for each of them. In our case the
observations refer to the pixels present in each image (in our case the dimension is 25x25=625;
the vector size of each image is 625). On the contrary, the output space (trained network, network
or SOM) refers to the low-dimensional universe in which the algorithm represents the input data.
It usually has two-dimensions, and is composed of a set of elements called neurons (or nodes)

34
which are interconnected, hence the network. What the algorithm does is to represent the input
space onto the output space, keeping all the relevant information and ordering observations in a
way such that topological closeness in the output space implies statistical similarity in the input
space.
The input space is composed by n-dimensional vectors we want to visualize/cluster in a low-
dimensional environment. We can express the input vector t as:
x=[ξ1(t),ξ2(t),…,ξn(t)]T
∈Rn
,
where ξi(t) represents the value for each dimension.
The output space is an array of x by y neurons (nodes) topologically connected following a kind
of geometrical rule (the most common topologies being circles, squares and hexagons). In our
case x =11 and y = 11. Each of the nodes is assigned a parametric real vector of initially random
values that we call model, and express as:
mi=[µin,µin,…,µin]T
∈Rn
Last, we may also define as d(x, mi) any distance metric between two vectors x and mi. The most
widely used is the Euclidean distance, although other specifications are also valid.
Fig. 2 Self Organizing Map (SOM)
What we are looking for is a topologically-ordered representation of the signal space into the
network. That is done by the SOM in an iterative process called training, in which each signal
vector is sequentially presented to the output space. The best matching unit (b.m.u.) for x is
defined as the neuron minimizing the distance to x. When this is found, the b.m.u. is activated and
an adaptive process starts by which such neuron and its topological neighbours are modified by
the following scheme:
mi(t+1)=mi(t)+hci(t)[x(t)-mi(t)],
where t and t + 1 represent, respectively, the initial and the final state after the signal has
activated the neuron; hci(t) is called neighbourhood function and expresses how the b.m.u. and its
neighbours are modified when activated by a signal; usually, the linear or Gaussian versions are
used. This process is repeated over many cycles before the training is finished. The
neighbourhood function depends on several parameters relevant for this stage: the distance
between the b.m.u. and the modified neuron (so the further away the neuron is, the smaller the

35
adjustment); a learning rate α(t) that defines the magnitude of the adjustment, and gradually
decreases as the training cycles advance; and the neighbourhood radius, which decides which of
the surrounding neurons of the b.m.u. are also modified, and also decreases over the training stage
and the self arranging (organization) of the input observations.
This procedure may be used as a visualization tool for multidimensional datasets as well as a
clustering method. In the first case, we would want to see how the different observations are
mapped into the SOM to discover (dis)similarities, making use of the topological preservation of
the statistical characteristics, and study how the different dimensions are distributed; in the
second one, the network would have a relatively small number of neurons (as many as clusters we
would want to obtain) and we would focus on analysing which observations are grouped with
which. In our case, images which have similar face characteristics gets grouped together within
the respective nodes/maps.
4. RESULTS
We applied the SOM-F method to around 113 image datasets obtained from IIT Delhi [27] and
made lot of studies. The results are shown in diagrammatic form as below:
Fig. 3 Counts plot of the map obtained from the face dataset. Empty units are depicted in gray.
The colour in each cell represents the number of face images which went into that went into that
cell.

36
Fig 4. shows the quality of the mapping;
In Fig. 3, the background colour of a unit corresponds to the number of samples mapped to that
particular unit; they are reasonably spread out over the map. Only one of the unit is empty: no
samples have been mapped to that unit. Fig. 4 shows the mean distance of objects, mapped to a
particular unit, to the vector of that unit. A good mapping should show small distances
everywhere in the map.
Fig. 5 Mapping of the 113 face images in a eleven-by-eleven SOM

37
Fig. 5 shows the mapping of images related to SOM. From the dataset, one shall infer that each
subject has 20 face images related to him. Figure 5 reveals this out clearly.
Fig. 6 SOM for the Face data.
Fig.6 shows the mapping of images when the SOM map has been formed; the background colour
indicate the person number (from 1 to 113 as explained in the colour palette); the radius of the
circles drawn inside a node also reemphasizes this point (face image corresponding to person
numbered 1 shall have a smaller radius when compared to the face image corresponding to a
person numbered 113);
Fig. 7 Training progress, as measured by the average distance of an object with the closest
codebook vector unit for face dataset.

38
During training, the nodes vectors become more and more similar to the closest objects in the
dataset. We optimize the parameters by visualisation of this process as shown in Fig. 7. The
learning took approximately 9 seconds and the recognition took less than a second.
5. CONCLUSIONS
A new feature extraction metric (named SOM-F) has been proposed. We have shown that our
proposed SOM-F method improves the performance and robustness of recognition when
compared to methods proposed in literature.
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Authors
Raja. A. S is currently a Research Scholar in Sathyabama University, Jeppiar Nagar,
Chennai,Tamil Nadu, India. His recent research interests include Biometrics,
Management, Graph Theory etc. He has also been awarded the Young IT
Professional Award’ for the year 2011 by Computer Society of India.
V. Joseph Raj received his PhD degree from M.S. University, Tirunelveli, India and
P.G in Anna University, Chennai, India. He is presently working as a Professor in
Kamaraj College, Thoothukudi, India. He has also worked as a Professor in European
University of LEFKE, North Cyprus, Turkey. He is guiding PhD scholars of various
Indian and Foreign universities. He has a vast teaching experience of about 20 years.
His research interests include neural network and biometrics.

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