A survey on face recognition techniques

Computer Engineering and Intelligent Systems www.iiste.org
ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)
Vol.4, No.6, 2013 - Selected from International Conference on Recent Trends in Applied Sciences with Engineering Applications
11
A Survey on Face Recognition Techniques
Jay Prakash Maurya, M.Tech*(BIST), Bhopal(M.P)
Mr.Akhilesh A. Waoo, Prof. BIST, Bhopal(M.P)
DR. P.S Patheja, Prof. BIST, Bhopal(M.P)
DR Sanjay Sharma, Prof. MANIT, Bhopal(M.P)
jpeemaurya@gmail.com akhilesh_waoo@rediffmail pspatheja@gmail.com ssharma66@rediffmail.com
Abstract
Face detection is a computer technology that determines the locations and sizes of human faces in arbitrary
(digital) images. It detects facial features and ignores anything else, such as buildings, trees and bodies. Face
detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find
the locations and sizes of all objects in an image that belong to a given class. Examples include upper torsos,
pedestrians, and cars. Face detection can be regarded as a more general case of face localization. These days face
detection is current research area. The face detection is normally done using ANN, CBIR, LDA and PCA.
Keywords:- ANN, CBIR, LDA and PCA
I.INTRODUCTION
Early face-detection algorithms focused on the detection of frontal human faces, solve the more general and
difficult problem of multi-view face detection. That is, the detection of faces that are either rotated along the axis
from the face to the observer (in-plane rotation), or rotated along the vertical or left-right axis (out-of-plane
rotation), or both. The algorithms take into account variations in the image or video by factors such as face
appearance, lighting, and pose. Many algorithms implement the face-detection task as a binary
pattern-classification task. That is, the content of a given part of an image is transformed into features, after
which a classifier trained on example faces decides whether that particular region of the image is a face, or not.
Often, a window-sliding technique is employed. That is, the classifier is used to classify the (usually square or
rectangular) portions of an image, at all locations and scales, as either faces or non-faces (background pattern).
Images with a plain or a static background are easy to process. Remove the background and only the faces will
be left, assuming the image only contains a frontal face. Using skin color to find face segments is a vulnerable
technique. The database may not contain all the skin colors possible. Lighting can also affect the results.
Non-animate objects with the same color as skin can be picked up since the technique uses color segmentation.
The advantages are the lack of restriction to orientation or size of faces and a good algorithm can handle
complex backgrounds. [1].
A face model can contain the appearance, shape, and motion of faces. There are several shapes of faces. Some
common ones are oval, rectangle, round, square, heart, and triangle. Motions include, but not limited to, blinking,
raised eyebrows, flared nostrils, wrinkled forehead, and opened mouth. The face models will not be able to
represent any person making any expression, but the technique does result in an acceptable degree of accuracy.
[3] The models are passed over the image to find faces, however this technique works better with face tracking.
Once the face is detected, the model is laid over the face and the system is able to track face movements.
II.TECHNIQUES OF FACE DETECTION
Methods for human face detection from color videos or images are to combine various methods of detecting
color, shape, and texture. First, use a skin color model to single out objects of that color. Next, use face models to
eliminate false detections from the color models and to extract facial features such as eyes, nose, and mouth.
LDA
Linear discriminant analysis (LDA) and the related Fisher's linear discriminant are methods used in statistics,
pattern recognition and machine learning to find a linear combination of features which characterizes or
separates two or more classes of objects or events. The resulting combination may be used as a linear classifier,
or, more commonly, for dimensionality reduction before later classification.
LDA is closely related to ANOVA (analysis of variance) and regression analysis, which also attempt to express
one dependent variable as a linear combination of other features or measurements.[1][2] In the other two
methods however, the dependent variable is a numerical quantity, while for LDA it is a categorical variable (i.e.
the class label). Logistic regression and probit regression are more similar to LDA, as they also explain a
categorical variable. These other methods are preferable in applications where it is not reasonable to assume that

12
the independent variables are normally distributed, which is a fundamental assumption of the LDA method.
LDA is also closely related to principal component analysis (PCA) and factor analysis in that they both look for
linear combinations of variables which best explain the data.[3] LDA explicitly attempts to model the difference
between the classes of data. PCA on the other hand does not take into account any difference in class, and factor
analysis builds the feature combinations based on differences rather than similarities. Discriminant analysis is
also different from factor analysis in that it is not an interdependence technique: a distinction between
independent variables and dependent variables (also called criterion variables) must be made.
LDA works when the measurements made on independent variables for each observation are continuous
quantities. When dealing with categorical independent variables, the equivalent technique is discriminant
correspondence analysis.[4][5]
EIGENFACE
Eigenfaces are a set of eigenvectors used in the computer vision problem of human face recognition. The
approach of using eigenfaces for recognition was developed by Sirovich and Kirby (1987) and used by Matthew
Turk and Alex Pentland in face classification. It is considered the first successful example of facial recognition
technology.[citation needed] These eigenvectors are derived from the covariance matrix of the probability
distribution of the high-dimensional vector space of possible faces of human beings.
Eigen Face Generation
A set of eigenfaces can be generated by performing a mathematical process called principal component analysis
(PCA) on a large set of images depicting different human faces. Informally, eigenfaces can be considered a set of
"standardized face ingredients", derived from statistical analysis of many pictures of faces. Any human face can
be considered to be a combination of these standard faces. For example, one's face might be composed of the
average face plus 10% from eigenface 1, 55% from eigenface 2, and even -3% from eigenface 3. Remarkably, it
does not take many eigenfaces combined together to achieve a fair approximation of most faces. Also, because a
person's face is not recorded by a digital photograph, but instead as just a list of values (one value for each
eigenface in the database used), much less space is taken for each person's face.
The eigenfaces that are created will appear as light and dark areas that are arranged in a specific pattern. This
pattern is how different features of a face are singled out to be evaluated and scored. There will be a pattern to
evaluate symmetry, if there is any style of facial hair, where the hairline is, or evaluate the size of the nose or
mouth. Other eigenfaces have patterns that are less simple to identify, and the image of the eigenface may look
very little like a face.
The technique used in creating eigenfaces and using them for recognition is also used outside of facial
recognition. This technique is also used for handwriting analysis, lip reading, voice recognition, sign
language/hand gestures interpretation and medical imaging analysis. Therefore, some do not use the term
eigenface, but prefer to use 'eigen image'.
IV.NEURAL NETWORK FACE DETECTION
Pattern recognition is a modern day machine intelligence problem with numerous applications in a wide field,
including Face recognition, Character recognition, Speech recognition as well as other types of object
recognition. The field of pattern recognition is still very much in it is infancy, although in recent years some of
the barriers that hampered such automated pattern recognition systems have been lifted due to advances in
computer hardware providing machines capable of faster and more complex computation.
Face recognition, although a trivial task for the human brain has proved to be extremely difficult to imitate
artificially. It is commonly used in applications such as human-machine interfaces and automatic access control
systems. Face recognition involves comparing an image with a database of stored faces in order to identify the
individual in that input image. The related task of face detection has direct relevance to face recognition because
images must be analyzed and faces identified, before they can be recognized. Detecting faces in an image can
also help to focus the computational resources of the face recognition system, optimizing the systems speed and
performance.
Face detection involves separating image windows into two classes; one containing faces (targets), and one
containing the background (clutter). It is difficult because although commonalities exist between faces, they can
vary considerably in terms of age, skin colour and facial expression. The problem is further complicated by
differing lighting conditions, image qualities and geometries, as well as the possibility of partial occlusion and
disguise. An ideal face detector would therefore be able to detect the presence of any face under any set of
lighting conditions, upon any background. For basic pattern recognition systems, some of these effects can be
avoided by assuming and ensuring a uniform background and fixed uniform lighting conditions. This assumption

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is acceptable for some applications such as the automated separation of nuts from screws on a production line,
where lighting conditions can be controlled, and the image background will be uniform. For many applications
however, this is unsuitable, and systems must be designed to accurately classify images subject to a variety
unpredictable conditions.
Each technique takes a slightly different approach to the face detection problem, and although most
produce encouraging results, they are not without their limitations. The next section outlines the various
approaches, and gives six examples of different face detection systems developed.
III.CBIR
Recent years increase in the size of digital image collections. This ever increasing amount of multimedia data
creates a need for new sophisticated methods to retrieve the information one is looking for. The classical
approach alone cannot keep up with the rapid growth of available data anymore. Thus content-based image
retrieval attracted many researchers of various fields. There exist many systems for image retrieval meanwhile.
Retrieval of Images from Image archive using Suitable features extracted from the content of Image is currently
an active research area. The CBIR problem is identified because there is a need to retrieve the huge databases
having images efficiently and effectively. For the purpose of content-based image retrieval (CBIR) an up-to-date
comparison of state-of-the-art low-level color and texture feature extraction approach is discussed. In this paper
we propose A New Approach for CBIR with interactive user feedback based image classification by Using
Suitable Classifier.
Figure.1- Image Retrieval Using Relevance Feedback
This Approach is applied to improve retrieval performance. Our aim is to select the most informative images
with respect to the query image by ranking the retrieved images. This approach uses suitable feedback to
repeatedly train the Histogram Intersection Kernel based Classifier. Proposed Approach retrieves mostly
informative and correlated images. Figure 1 shows systematic retrieval process.
Color Feature
Color is the most popularly used features in image retrieval and indexing. On the other hand, due to its inherent
nature of inaccuracy in description of the same semantic content by different color quantization and /or by the

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uncertainty of human perception, it is important to capture this inaccuracy when defining the features. We apply
fuzzy logic to the traditional color histogram to help capture this uncertainty in color indexing [3], [2].In image
retrieval systems color histogram is the most commonly used feature. The main reason is that it is independent of
image size and orientation. Also it is one of the most straight-forward features utilized by humans for visual
recognition and discrimination. Statistically, it denotes the joint probability of the intensities of the three color
channels. Once the image is segmented, from each region the color histogram is extracted. The major statistical
data that are extracted are histogram mean, standard deviation, and median for each color channel i.e. Red,
Green, and Blue. So totally 3 × 3 = 9 features per segment are obtained. All the segments need not be considered,
but only segments that are dominant may be considered. Because this would speed up the Calculation and may
not significantly affect the end result.
Texture Feature
Texture is another important property of images. Various texture representations have been investigated in
pattern recognition and computer vision. Basically, texture representation methods can be classified into two
categories: structural and statistical. Structural methods, including morphological operator and adjacency graph,
describe texture by identifying structural primitives and their placement rules. They tend to be most effective
when applied to textures that are very regular. There is no precise definition for texture. However, one can define
texture as the visual patterns that have properties of homogeneity that do not result from the presence of only a
single color or intensity. Texture determination is ideally suited for medical image retrievals].In this work,
computation of gray level co occurrence matrix is done and from which a number of statistical measures are
derived. The autocorrelation function of an image is used to quantify the regularity and the coarseness of a
texture. This function is defined for an image I as: The notion of texture generally refers to properties of
homogeneity direction information.
Shape Feature
Shape is used as another feature in image retrieval. However, it is evident that Retrieval by shape is useful only
in very restricted environments, which provide a good basis for segmentation (e.g. art items in front of a
homogeneous background). Shape descriptors are diverse, e.g. turning angle functions, deformable templates,
algebraic moments, and Fourier coefficients.
Combinations of Color, Texture, Shape
Features Similarity is based on visual characteristics such as dominant colors, shapes and textures. Many systems
provide the possibility to Combine or select between one or more models. In a combination of color, texture and
contour features is used. Extends the color histogram with textural information by weighting each Pixel’s
contribution with its Laplacian also provides several different techniques for image retrieval. These methods use
the feedback from the user in order to automatically select or weight the different models, such that the
requirements of the user are best fulfilled. Several systems perform segmentation and characterize each region
by color, texture, and shape. As stated before these systems crucially depend on a good segmentation which is
not always given. This can result in retrieval results which seem to have nothing in common with the query.
Among various flowers the system returns an image that does not display a star-shaped object but for which the
segmentation delivered a star-like region as an artificial boundary within a textured region. We therefore
consider segmentation-based methods reliable only for applications that allow for a precise segmentation.
Similarity Computation
Similarity measurement is a key to CBIR algorithms. These algorithms search image database to find images
similar to a given query, so, they should be able to evaluate the amount of similarities between images. Therefore,
feature vectors, extracted from the database image and from the query, are often passed through the distance
function d. The aim of any distance function (or similarity measure) is to calculate how close the feature vectors
are to each other. There exist several common techniques for measuring the distance (dissimilarity) between two
N-dimensional feature vector f and g. Each metric has some important characteristics related to an application.
Image Classification
It is processing techniques which apply quantitative methods to the values in a digital yield or remotely sensed
scene to group pixels with similar digital number values into feature classes or categories Image classification
and annotation are important problems in computer vision, but rarely considered together. Intuitively,
annotations provide evidence for the class label, and the class label provides evidence for annotations. Image
classification is conducted in three modes: supervised, unsupervised, and hybrid. General, a supervised
classification requires the manual identification of known surface features within the imagery and then using a
statistical package to determine the spectral signature of the identified feature. The spectral fingerprints of the
identified features are then used to classify the rest of the image. An unsupervised classification scheme uses
spatial statistics (e.g. the ISODATA algorithm) to classify the image into a predetermined number of categories

15
(classes). These classes are statistically significant within the imagery, but may not represent actual surface
features of interest. Hybrid classification uses both techniques to make the process more efficient and accurate.
We can use SVM (Support Vector Machine) for classification. Support Vector Machine, an important machine
learning technique has been used efficiently for variety of classification purposes like object based image
analysis, hand written digit recognition, and image segmentation among others. SVM can be used efficiently as a
binary classifier as well as multi class classifier purpose. A Support Vector Machine (SVM) performs
classification by constructing an N-dimensional hyper plane that optimally separates the data into two categories.
SVM models are closely related to neural networks. In fact, a SVM model using a sigmoid kernel function is
equivalent to a two-layer, perceptron neural network. Support Vector Machine (SVM) models are a close cousin
to classical multilayer perceptron neural networks. Using a kernel function, SVM‟s are an alternative training
method for polynomial, radial basis function and multi-layer perceptron classifiers in which the weights of the
network are found by solving a quadratic programming problem with linear constraints, rather than by solving a
non-convex, unconstrained minimization problem as in standard neural network training. Linear SVM is the
simplest type of SVM classifier which separates the points into two classes using a linear hyper plane that will
maximize the margin between the positive and negative set. The concept of non linearity comes when the data
points cannot be classified into two different classes using a simple hyper plane, rather a nonlinear curve is
required. In this case, the data points are mapped non-linearly to a higher dimensional space so that they become
linearly separable. Table-1 given explain comparison between image classification techniques.
Parameter Artificial neural
network
Support vector
machine
Fuzzy logic Genetic algorithm
Type of approach Non parametric Non parametric
with binary
classifier
Stochastic Large time series data
Non linear decision
boundaries
Efficient when the
data have only few
input variables
Efficient when the
data have more
input variables
Depends on prior
knowledge for decision
boundaries
Depends on the direction
of decisions
Training speed Network structure,
momentum
rate ,learning rate,
converging criteria
Training data size,
kernel parameter
Iterative application of
the fuzzy integral.
Referring irrelevant and
noise genes.
Accuracy Depends on number
of input classes.
Depends on
selection of
optimal hyper
plane.
Selection of cutting
threshold
Selection of genes.
General performanceNetwork structure Kernel parameter Fused fuzzy integral. Feature selection.
Table 1. Comparision of image classification techniques
CONCLUSION & FUTURE WORK
As the research point of view any study is not enough that you can say perfect. This paper gives some interest in
the following are for research. In these days the crime we should be care of our safety this field of research can
held in this area. The two algorithms can be combining to find the batter results in respect to accuracy & broader
classes.
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