40120140501006

International Journal of ElectronicsJOURNAL OF ELECTRONICS AND
INTERNATIONAL and Communication Engineering & Technology (IJECET),
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 1, January (2014), © IAEME

COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)

ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 5, Issue 1, January (2014), pp. 52-58
© IAEME: www.iaeme.com/ijecet.asp
Journal Impact Factor (2013): 5.8896 (Calculated by GISI)
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IJECET
©IAEME

IMAGE RETRIEVAL AND FACE RECOGNITION TECHNIQUES:
LITERATURE SURVEY
Mrs. Manisha Bhisekar
M.E. - 2nd year -E&TC -Digital Systems, G.S. Moze C.O.E., Balewadi, Pune
Prof. Prajakta Deshmane

ABSTRACT
Now days the process of image retrieval is widely used in may real life applications from
large datasets. This process of retrieving the images from the big images dataset is called as content
based image retrieval (CBIR). These CBIR techniques are adopted recently in many image based
applications like fingerprint matching image retrieval, face based image retrieval, and attribute based
image retrieval. The area of interest during this review paper is face based image retrieval. We have
studied many methods presented recently for the face based image retrieval in which different face
recognition algorithms were used. The goal of face based image retrieval is to display the face results
those are exactly related to the query image of person. In this paper, first we present the review of
CBIR methods, after that different face recognition methods discussed with their advantages and
disadvantages.
Keywords: Image Retrieval, CBIR, Face-Based Image Retrieval, Face Recognition, Query Image.
I. INTRODUCTION
Since from last decade, there was tremendous growth in use of technology and science in
different fields which is resulted into the huge amount of images in different areas such as art
galleries, nature, entertainment, education, industry, biomedical, security etc. In these applications,
we frequently need to store and retrieve for various purposes, especially for decision making.
Retrieving the particular image from huge image dataset is very complex and tedious tasks; therefore
there must be an automated system which can able to retrieve number matching images to the input
query image. The designing of such systems is quite challenging in research. There are many image
retrieval systems presented so far for automatic retrieval of images from large image dataset. There
are two types of image retrieval systems such as content based and text based image retrieval. For the
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International Journal of Electronics and Communication Engineering & Technology (IJECET),

text based system, text annotation is done manually for all images and then used by a database
management system to perform image retrieval. This manual process may takes more time for doing
so. There are two main limitations of this approach such as more resources and costs are required to
do the manual image annotations and the process of explaining the contents of image highly
subjective [1].
That is, the perspective of textual descriptions given by an annotator could be different from
the perspective of a user. In other words, there are textual user queries and image annotation or
description is inconsistencies between. Incompatibility to correct the problem, the image according
to image content retrieval is carried out such a strategy the so-called content-based image Retrieval
(CBIR). Efficient and effective retrieval [1], [2] to facilitate the images to build meaningful
description of the physical characteristics of CBIR system is the primary goal of CBIR system
basically features Extraction and matching these features works on each dataset with queries image
in color, shape and texture features. Based on this extracted features contents are matched and hence
the results generated automatically without any manual work. Later this approach of CBIR is used in
under different real time environments such as security, medical imaging, photography etc. Recently
this concept is well utilized in face based image retrieval method.
The face based image retrieval methods are nothing but the combination of face recognition
and CBIR techniques. This method is basically used for the extraction of persons images from large
dataset based on input query image of same person. Internally this process works same As CBIR,
only its uses the extra features like face detection, face recognition and face alignment techniques.
A first, though very limited, approach to achieve this goal has been implemented in the Photo Book
system [8] which allowed for retrieving images of persons when the face covered a large part of the
image and was taken under normalized conditions. In [2] an approach to the same problem is
proposed. Portrait images, glasses, hats, facial expressions and facial hair with strong respect for
retrieving a more complex approach [7] is presented in a component-based descriptors for images
using LDA face changes [11] is presented in [1] portrait images taken from the Web are indexed in
such systems are proposed for comparing performance measures. These methods have in common is
that all of the image same face. In addition it assumes that to be recognized is the image of the face,
the General image retrieval is not an appropriate assumption for a significant portion forms. In
contrast to these approaches we propose a method that is able to deal with images displaying several
persons and in which the faces do not necessarily form large parts of the image.
In this paper we are presenting our review of two different approaches, below in section II we
will discuss about CBIR system and its different aspects, in section III different face recognition,
detection methods used for face based image retrieval systems. This paper is our roadmap for future
work.
II. REVIEW OF IMAGE RETRIEVAL TECHNIQUES
With the rapid growth of digital devices for capturing and storing multimedia data,
Multimedia information retrieval is one of the most important research subjects among which image
retrieval key challenge has been one of the problems in recent years. in content-based image retrieval
(CBIR) which image retrieval in the last decade many research interests in computer communities
has attracted a wide range of the most important topics [6]. Although extensive studies have been
conducted, finding desired images from multimedia databases is still a challenging and open issue.
The main challenges are due to two gaps in CBIR [6]. The first is the sensor gap between the object
of the world and the information represented by computers. The second one is the semantic gap
between the low-level visual features and high-level human perception and interpretation. Many
early year studies on CBIR focused primarily on feature analysis which mainly aimed at solving the
sensory gap [8], [7].
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However, because of the complexity of image understanding and the challenge of semantic
gap, it is impossible to discriminate all images by employing some rigid simple similarity measure
on the low-level features. Although it is feasible to bridge the semantic gap by building an image
index with textual descriptions, manual indexing on image databases is typically time consuming,
costly and subjective, and hence difficult to be fully deployed in practical applications. Despite the
promising process recently reported in image annotations [9], fully automatic image annotation is
still a long way off. Relevance feedback, as an alternative and more feasible technique to mitigate the
semantic gap issue [10].
Increasing developments in computer and communication technologies have led to enormous
archives of digital images in various areas such as digital library, medicine, art galleries, remote
sensing, education, entertainment and so on. That is why transfer of digital images has been
considered as one of the most important research topics for more than 40 years and today most of the
efforts are that firstly to decrease the volume of storage and secondly to retrieve the proposed images
with acceptable speed and precision rate. In this area a wide range of researches revolves on search
based techniques on image databases as a critical need. The primary search-based solutions depend
on textural descriptions provided by human operators which two problems were associated with this
approach: expensiveness and low efficiency. The first one comes out of the amount of manual labor
time required for image annotation and the second one comes out of the fact that each image may
contain several. Before moving to in details about the CBIR systems, below we will discuss about
the image retrieval system:
2.1 Image Retrieval: Techniques of image retrieval integrate both addressing the more detailed
perceptual aspects, low-level visual features and high-level semantic features which the more
detailed concept of the visual data
Public and the private entities controls the rapid growth of the number and the types of the
assets of the multimedia which are get controlled by the entities which are public and private, and
also they are expanding the range of the video and the image documents which are appearing on the
web, they provide very good tools for retrieving very perfect and good image visual data. The
process of image retrieval is perfectly depends on the availability of the image contents. The
descriptor of the image is the visual features like texture, shape, color and somatic primitives and
spatial relationships.
Image feature retrieval of text image based solely on recovery values are conveniently
represented as a vector. However, "a picture is worth a thousand words." images, text content,
content and more versatile view of data is very large and still expanding very rapidly presenting the
Visual data. to cope with the new system of content-based image retrieval system The family of
image-based content is named recovery system high level low level of semantic features, view
features and more detailed addressing the conceptual aspects. For retrieving and managing the visual
data sufficient for two types of features only but it is not only case but researches face one problem
that not only to retrieve the features but combine those features also this is huge barrier in the front
of the researchers. The satisfactory performance is not provided by the heuristic approaches and
intuitive approach.
Therefore, mange low level features and high level concepts we need to managing latent
correlation. The main challenge in front of the researchers is that how to manage this bridge between
the somatic features and visual features.
The images associates’ different type of information with them this information is: The
different types of information that are normally associated with images are:
•

Metadata Content-independent: data is related to the image content but is not concerted with
image Examples are author’s name, image format, date, and location.
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•
-

-

Metadata Content-based:
Metadata Non-information-bearing: the data that referring to the intermediate level or to the
low-level features such as texture color. Shape, relationships, data referring to low-level or
intermediate-level features, such as color, texture, shape, spatial relationships, and various
combinations for them. Such information is computed by using the row data.
Metadata Information-bearing: semantic are referred by the data, real work entities and
concerned with relationships. For this type of the method the is Empire State Building it is
such as particular building which are appear into the image, this building is cannot derived
from raw data, must be supplied by the other means, perhaps inheriting the this semantic
label from the another image, where a similar appearing building has already been identified.

2.2 Existing Techniques: Extraction visual features are the basis of any content-based image
retrieval technique. Basically used features such as include texture, color, shape and special
relationship. Due to composition of complex visual data and subjectivity of perception ,there is
actually not exist single best representation for given feature visual, Various approaches is
introduced for each of these visual features and each of them characterizes the feature from a
different perspective. In content-based image retrieval, color is one of the mostly used visual
features. it is very easy and robust to represent. Various studies of spaces color and perception color
is proposed, in order to find out color based techniques that are mostly closed to human color
perceive align. The color histogram most widely used presentation technique, statistically describing
from combined probabilistic properties of the various color channels by capturing the number of
pixels having particular properties. For example, a color histogram might describe the number of
pixels of each red channel value in the range [0, 255]. Three of its derived color histograms,
particular channels values is shown along such as x-axis and numbers of pixels shown along with yaxis and particular color channels used that indicated in each histogramIt's a well-known such as
histograms information related to spatial distribution of colors and two very different images can be
very similar histograms. There is more work to capture such local histograms information has been
extending more spatial detail information, Histograms. Correlograms and angiograms are the two
main approaches used to capture such as Correlograms. Special areas around the pixels in special
colors pixel Distribution of colors is used to capture and angiograms is used for General properties of
spatial arrangement of a special signature capture. Angiograms texture and size can also be used for
features.
2.3 Content-Based Image Retrieval (CBIR) Systems: There are various excellent surveys with
CBIR (Content based image). QBIC (Query-by-Image-Content) was one of the first prototype
systems. QBIC was developed at the IBM Alma den Research Center and now is currently folded
into DB2. It is used to allow queries by shape, texture, and color, and introduce a sophisticated
similarity function. Similarity function has a quadratic time-complexity. To speed-up searches,
multidimensional indexing is used is another property of QBIC. The MARS system is developed at
the University of Illinois at Urbana-Champaign, and that allows for sophisticated relevance feedback
from the user.
III. REVIEW OF FACE RECOGNITION METHODS
To allow for investigating the effect of face detection and representation methods, we tested
two methods: detection and representation using Eigenfaces [10] and detection using the Viola &
Jones method [11] and representing the face as a size normalized image patch. Both methods are data
driven methods and are explained briefly in the following. A general overview on methods for face
detection and representation can be found in [12].
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3.1 Eigenfaces
Turk and Pent land applied principal component analysis to face recognition and detection
[10]. Principal component analysis is performed on a training set of face images to generate the
Eigen-vectors (here called Eigenfaces) which span a subspace (called the face space) of the image
space. To determine whether an image is a face or not, it is estimated, for instance subspace and back
only 20 first face in using the space components. Again, the original image and back-projection can
calculate the distance between. Due to the nature of the Eigenfaces, like face an image, while
gaiSufaces are very poor and thus reconstructed the original image and back-projection is high in this
case, the distance between the reconstructed. Thus, it is a measure of the distance as faceness. If
faceness is calculated for every position in the image, a face can occur and a face map to find local
minima can be traced from an advantage of this method is that it is a compact and generalizing the
method of representing faces offers.
3.2 Viola & Jones Method
Viola and Jones present a new and radically faster approach to face detection based on the
AdaBoost algorithm from machine learning [11]. Boosting is a method of combining several weak
classifiers to generate a strong classifier. Weak classifiers AdaBoost algorithm on statistical training
and generalization error bounds is providing strong classifiers to produce a well-known algorithm.
weak classifiers characteristics of three types in Viola & Jones algorithm are based on a tworectangle SuSum of the values of two adjacent rectangular Windows mode is the difference between
a three-rectangle feature in three adjacent rectangles understands and extreme rectangles pixels in the
center of Yoga and yogic rectangle computes the difference between. A four-set of 2 × 2 rectangle
feature rectangles considers and calculates that main diagonals forming rectangles and the difference
between the amounts of pixels off a 24 × 14 sub-window there are more than 180,000 features.
3.3 Faces in Image Retrieval
To be able to use the faces in our image retrieval framework, we have to define a distance
measure for two images X and Y in which faces X1 . . . XF and Y1 . . . YV have been detected. One
can think about various ways of matching, e.g. taking into account face positions. For experiments
presented here, we match the most simple to use as possible. That’s it, we're all pairs x and two
images (Xi, Yj) between faces Y Euclidean distance d (Xi, Yj) calculate the distance d (Xi, Yj). The
small distance between X and Y of the images used in the images of individuals in a query image
Retrieving that can be interpreted as, and this person's face as the same query as possible to face it in
the image.
IV. DATABASE USED
We are showing experiments multiple databases:
a) Bio-ID database: a database present one person for every image which can say to be recorded in
controlled conditions.
b) RWTH-i6 Groups of People Database: a newly created database which has been collected using
Google image search. It is obvious that this is a much harder task.
4.1 Bio-ID
The dataset consists of 1521 gray level images with a resolution of 384×286 pixel. Each
image shows the frontal view of a face of one out of 23 different test persons. The database is
available online. This task is very simple and the results appear to be very good. Unfortunately, the
data are not properly labeled, thus a quantitative evaluation is not easily possible.

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4.2 RWTH-i6 Groups of People Database
Images of persons, single persons, and individuals without all of the images of groups of
images due to the lack of a database containing, we decided to make our own database. To do this we
politicians, musicians and music bands (such as Gerhard Schroeder, Britney Spears, Depeche Mode)
using the names of Google Image Search FAQ and took each of the 60 images for these keywords. in
total we used 38 search terms then We all have images that the search term, such as images showing
dogs, comics, or other people would show up as images were deleted not relevant to it containing
867 images with 38 classes led to a database.
V. CONCLUSION AND FUTURE WORK
In this paper we have presented the review of image retrieval system and face based image
retrieval system, especially the techniques of face detection and representation in image based face
recognition system. We explained detailed working of content based image retrieval system as well
as face based image retrieval system. In the literature there are many methods presented for face
based image retrieval systems. But each method is suffered from different limitations. As per our
study ignore strong, the lack of a specific geometric face-face image in view between words. Face
recognition works in various recent discriminative facial features is proposed, but these features
typically not global, high-dimensional and thus is suitable for quantization and inverted indexing.
Future work we present efficient new method said on the problems and suggestions for the above to
work.
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