Informatics Engineering, an International Journal (IEIJ) - Face Recognition: A Survey

Informatics Engineering, an International Journal (IEIJ) ,Vol.1, No.1, December 2013
31
Face Recognition: A Survey
Shailaja A Patil1
and Dr. P. J. Deore2
1,2
Department of Electronics & Telecommunication Engineering, R. C. Patel Institute
of Technology, Dist: Maharashtra.
ABSTRACT
Face Recognition plays a major role in Biometrics. Feature selection is a measure issue in face
recognition. This paper proposes a survey on face recognition. There are many methods to extract face
features. In some advanced methods it can be extracted faster in a single scan through the raw image and
lie in a lower dimensional space, but still retaining facial information efficiently. The methods which are
used to extract features are robust to low-resolution images. The method is a trainable system for selecting
face features. After the feature selection procedure next procedure is matching for face recognition. The
recognition accuracy is increased by advanced methods.
KEYWORDS
Face features, feature selection, local binary pattern.
1. INTRODUCTION
The face is one of the most acceptable biometrics, and it has also been the most common method
of recognition that human use in their visual interactions. The problem with authentication
systems based on fingerprint, voice, iris and the most recent gene structure (DNA fingerprint) has
been the problem of data acquisition. For example, for fingerprint the concerned person should
keep his/her finger in proper position and orientation and in case of speaker recognition the
microphone should be kept in proper position and distance from the speaker. But, the method of
acquiring face images is non-intrusive and thus face can be used as a biometric trait for covert
(where user is unaware that he is being subjected) system. Face is a universal feature of human
beings. Face recognition is important not only due to the capability of its lot of potential
applications in research fields but also due to the capability of its solution which would help in
solving other classification problems like object recognition.
In face recognition system it identifies faces present in the images and videos automatically. It is
classified into two modes:
1. face verification (or authentication)
2. face identification (or recognition)
In face verification or authentication there is a one-to-one matching that compares a query face
image against a template face image whose identity is being claimed. In face identification or
recognition there is a one-to-many matching that compare a query face image against all the

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template face images in the database to determine the identity of the query face image. Another
face recognition scenario involves a watch-list check, where a query face is matched to a list of
suspects (one-to-few matches). The performance of face recognition systems has improved
significantly since the first automatic face recognition system was developed by Kanade (T.
Kanade, 1973). Furthermore, face detection, facial feature extraction, and recognition can now be
performed in real-time for images captured under favorable (i.e. constrained) situations.
Although progress in face recognition has been encouraging, but still there are some
unconstrained tasks where viewpoint, illumination, expression, occlusion, accessories, and so on
vary considerably.
It is natural, nonintrusive, and easy to use. There are many biometric systems but among the six
famous biometric attributes considered by Hietmeyer ( R. Hietmeyer, 2000), In a Machine
Readable Travel Documents (MRTD) system facial features scored the highest compatibility,
such as enrollment, security system, machine requirements, renewal, surveillance system and
public perception, shown in Figure 1.
Face Finger Hand Voice Eye Signature
Figure 1: A comparison of various biometric features based on MRTD compatibility (R Hietmeyer,2000).
Face Recognition Processing
Face recognition is a visual pattern recognition problem. There, a face as a three-dimensional
object subject to varying illumination, pose, expression and so on is to be identified based on its
two-dimensional image (three-dimensional images e.g., obtained from laser may also be used). A
face recognition system generally consists of four modules as depicted in Figure 2: detection,
alignment, feature extraction, and matching, where localization and normalization (face detection
and alignment) are processing steps before face recognition (facial feature extraction and
matching) is performed.
Face detection segments the face areas from the background. In the case of video, the detected
faces may need to be tracked using a face tracking component. Face alignment is aimed at
achieving more accurate localization and at normalizing faces thereby whereas face detection
provides coarse estimates of the location and scale of each detected face. Facial components, such

33
as eyes, nose, and mouth and facial outline, are located; based on the location points, the input
face image is normalized with respect to geometrical properties, such as size and pose, using
geometrical transforms or morphing. The face is usually further normalized with respect to
photometrical properties such illumination and gray scale.
After a face is normalized geometrically and photo-metrically, feature extraction is performed to
provide effective information that is useful for distinguishing between faces of different persons
and stable with respect to the geometrical and photometrical variations. For face matching, the
extracted feature vector of the input face is matched against those of enrolled faces in the
database; it outputs the identity of the face when a match is found with sufficient confidence or
indicates an unknown face otherwise.
Face recognition results depend highly on features that are extracted to represent the face pattern
and classification methods used to distinguish between faces whereas face localization and
normalization are the basis for extracting effective features. These problems may be analyzed
from the viewpoint of face subspaces or manifolds, as follows.
Figure 2: Face Recognition processing flow.
2. LITERATURE SURVEY
Face recognition has been an active research area over last 40 years. The face recognition
research has several disciplines such as image processing, machine learning approach, pattern
recognition, computer vision, and neural networks. Face recognition has many applications in the
fields of biometrics, security system, surveillance systems, and access control and law
enforcement. The limitation of face recognition system can be stated as given still face images or
video of a scene, identifying or detecting one or more persons in the given video by using a stored
database of face images [R. Chellappa et. al. 1995]. Classification is the main problem. In the
process of face recognition it includes, to train the face images from the known individuals and
then to classify the newly coming test images into one of the classes.
The problem of face recognition is easily solved by humans where limited memory can be the
main problem. The problems or limitations for a machine learning face recognition system are:

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1. Facial expression change
2. Illumination variation
3. Ageing
4. Pose change
5. Scaling factor (i.e. size of the image)
6. Frontal vs. profile
7. Presence and absence of spectacles, beard, mustache etc.
8. Occlusion due to scarf, mask or obstacles in front.
In automatic face recognition system the main complicated task is that it involves detection of
faces from a cluttered background, facial feature extraction, and face recognition. A complete
face recognition system has to solve all sub-problems, where each one is a separate research
problem.
Image template based and geometry feature-based are the two classes of face recognition system
algorithms. In template based method it (Robert J. 1981 ) compute the correlation between a face
image and one or more model of face image templates to estimate the face image identity from
the database. Brunelli and Poggio (R. Brunelli, 1993) suggest the optimal strategy for face
recognition system which is holistic and corresponds to template matching. The statistical tools
such as Support Vector Machines (SVM) (E. Osuna, 1997), (Vladimir N, 1995) Independent
component Analysis, Principal Component Analysis (PCA) (L. Sirovich, 1987), (Matthew Turk,
1991), Linear Discriminant Analysis (LDA) (Peter N. Belhumeur et.al, 1997), kernel methods
(Bernhard Scholkopf et.al, 1998 ), (M. H. Yang, 2002), and neural networks (A. Jonathan, 1995),
(Steve Lawrence, 1998), (T. Poggio, 1994) used to construct a suitable database of face image
templates.

35
Figure 3: Summary of approaches to face recognition.
Other than neural network approach and statistical approach there are other approaches known as
hybrid approaches which are the combination of both statistical pattern recognition techniques
and neural network systems. Examples for hybrid approaches include the combination of PCA
and Radial Basis Function (RBF) neural network (M. J. Er, 1999), (C. E. Thomaz et. al, 1998).
Among other methods, people have used range (R. Chellappa, 1995), infra-red scanned (Y.
Yoshitomi et. al, 1997) and profile (Z. Liposcak, 1999) images for face recognition. While
templates can be viewed as features, they mostly capture global features of the face image. Facial
occlusion (Face images with goggles, specs, scarf etc) and low resolution is often difficult to
handle in these given approaches.
In the geometry feature based methods the explicit local facial features are found, and their
geometric relationships. Cootes et al. (Andreas Lanitis et.al, 1997) have presented an active shape
model which was the extending approach by Yuille (Alan L, 1991). Wiskott et al.( Laurenz
Wiskott, 1997) developed an elastic bunch graph matching algorithm for face identification.
Penev et al. (P. Penev, 1996) developed PCA into Local Feature Analysis (LFA). This technique

36
is one of the most successful and useful commercial face recognition systems, FaceIt. The
summary of approaches to face recognition is shown in Fig. 3.
Template based Methods
Template matching is conceptually related to holistic approach which attempts to identify faces
using global representations (J. Huang, 1998). These types of methods approach the face image as
a whole and try to extract features from the whole face region and then classify the image by
applying a pattern classifier. One of the methods used to extract features in a holistic system, is
based on statistical approaches which are discussed in the following section.
Statistical Approaches
There are some techniques that identify, parameterize and analyze linear subspaces. Other than
linear subspaces there are some statistical face recognition techniques which are based on non-
linear subspaces (like kernel-PCA and kernel-LDA), transformation (like DCT, DCT & HMM
and Fourier Transform) and Support Vector Machine (SVM). Appearance-based approaches for
face recognition like PCA, LDA, and probabilistic subspace view a 2D face image as a vector in
image space.
Neural Network based Approaches
Artificial Neural Network (ANN) (B. Yegnanarayana, 1999) is a most successful tool for pattern
recognition problems. In Kohonen's associative map (T. Kohonen, 1998), one of the earliest
demonstrations of neural network for face image recall applications is reported. Using a small set
of face images, accurate recall was reported even when input image is very noisy, low resolution
and dimension or when portions of the images are missing. A few NN based face recognition
techniques are discussed in the following.
Single Layer adaptive NN: A single layer adaptive NN (one for each person) for face
recognition, expression analysis and face verification was reported in (T. J. Stonham, 1984). A
system named Wilke, Aleksander and Stonham's recognition devise (WISARD) was devised. It
needs typically 200-400 presentations for training each classifier where the training patterns
included translation and identification in facial expressions. One classifier was constructed
corresponding to one subject in the database.
Multilayer Perceptron (MLP): Most of the present literature on face recognition system with
neural networks present results with a small number of classes (often below 20). In (D. Demers,
1993) the first 50 principal components of the face images were extracted and reduced to five
dimensions using auto associative neural network. The resulting representation was classified
using a standard multilayer perceptron (MLP).
Self-Organizing map (SOM): The self-organizing map describes a quantization of the face
image samples into a topological space are also nearby in the output space, it provides
dimensionality reduction and invariance to minor changes in the face image sample. The
convolutional neural network provides partial invariance to translation, rotation, scale and
deformation.

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Hop-field memory model: In (Y. Dai, 1998), a Hop-field memory model for the facial images
is organized and the optimal procedure of learning is determined. A method for face recognition
using Hop-field memory model combined with the pattern matching is proposed. It shows better
performance of database having 20 faces of 40 subjects.
Others: A hierarchical neural network is grown automatically and not trained with gradient
descent was used for face recognition or identification by Weng (J. Weng, 1995). They found
good and more accurate results for discrimination of ten subjects. The ability of the compression
networks was demonstrated by Cottrell and Fleming in (G. W. Cottrell, 1990).
In (Vladimir N, 1995) linear auto associative networks, non-linear auto-associative (or
compression) and/or hetero-associative back propagation networks are explored for face
processing. In (Shang-Hung, 1997) Lin et al. proposed a face recognition technique based on
Probabilistic Decision based Neural network (PDBNN). It adopts a hierarchical network
structures with non-linear basis functions and competitive credit assignment scheme. It
demonstrated a successful application of PDBNN on FERET and ORL databases.
The mixture consists of ensembles of radial basis functions (RBFs). Inductive Decision Trees
(IDTs) and SVMs implement the “gating network" components. Experimental results yield good
results on gender, ethnic and pose classification, which can be effectively used in face
recognition.
Hybrid Approaches
The hybrid approaches use both statistical pattern face recognition techniques and neural
networks.
PCA and RBF: The use of RBF on the data extracted by discriminant eigen-features suggested
by Er et al. They used a hybrid means the combination of learning algorithm to decrease the
dimension of the search space in the gradient method, which is very complicated for optimization
of high dimension problem in face images. Firstly, they tried to extract the face image features by
principal component analysis, Independent component analysis and linear discriminant analysis
methods. Secondly, they developed a hybrid learning algorithm to train the RBF Neural
Networks, so the dimension of the search memory space is significantly decreased in the gradient
method. Thomaz et al. also studied on combining two methods PCA and RBF neural network.
Other Approaches
Range Data: The different method used in face recognition task is using the range face images.
The database of face image is obtained by scanning the individual person with a laser scanner
system in some methods. This system consist of the more information so the system processes 3-
dimensional data to classify face images.
Infra-red Scanning: Another method used for face recognition system is scanning the face
image by an infra-red light source. Thermal sensors are used to detect temperature distribution of
a face image data by Yoshitomi et al. In this method, the front view of face image in input image
is normalized in terms of location, dimension and size, resolution followed by measuring the

38
temperature distribution, the locally averaged temperature and the shape factors of face image.
The disadvantage of visible ray face image analysis is that the performance is strongly influenced
by lighting condition including variation of shadow, reflection and darkness ie the illumination
variation. These limitations of face recognition system can be overcome by the method using
infra-red rays.
Profile Images: Liposcak and Loncaric (Z. Liposcak, 1999) worked on profile face images
instead of frontal face images. The method is based on the representation of the original and
morphological derived profile face images. The aim of this method was to use the profile outline
template that bounds the face and the hair. They take a gray-level profile face image and
threshold it to produce a binary face image representing the face region. Then, they simulate hair
growth and haircut and produce two new profile face image silhouettes. From these three profile
face image shapes they obtain the feature vectors. After normalizing the vector components of
profile face image, they use the Euclidean distance matching measure for measuring and
matching the similarity of the feature vectors derived from different face image profiles.
Geometry Feature based Methods
Geometry feature based methods uses the facial feature measures such as distance between eyes,
ratio of distance between eyes and nose etc., but it is significantly different from the feature-based
techniques that it constructs the topological graph using the facial features of each subject.
Graph Matching based Methods: In (M. Lades, 1997) Lades et al. presented dynamic link
architecture for noise invariant object recognition which employs elastic bunch graph matching to
find the closed stored graph. Objects were produced with sparse graphs whose vertices were
labeled with geometrical distances. Only the magnitudes of the coefficients were used for
matching and recognition of face images. When recognizing or identifying a face of a new image,
each graph in the model gallery was matched to the image separately and the best match indicated
the recognized person which is the output result. They presented good results with a database of
87 subjects and test images composed of different facial expressions and faces turned 15 degree.
The matching process was taking roughly 25 seconds to compare an image with 87 stored objects
when using a parallel machine with 23 transputer's.
Wiskott et al. extended the system to handle larger variations in pose and to increase the matching
accuracy of face recognition. Then, they employ object adapted graphs of face images, so that
nodes refer to Specific facial landmarks or facial specification, called fiducial points. The
correspondences between two face images can be found across large viewpoint of face image
changes. Thirdly, a new data structure called the elastic bunch graph matching was introduced
which serves as generalized representation of faces by combining jets of a small set of individual
faces. This allows the system to find the fiducial points in one matching process of face
recognition, which eliminates the need for matching of face image of each model graph
individually. This also reduces computational effort significantly. It offers good performance of
about 98% for FERET database. But the drawback in this feature matching approach is that it
requires manual intervention to select the fiducial points in the facial image and it requires precise
location of those points.
Feature based PCA: Cagnoni and Poggi suggested a feature based approach to face
recognition system. They applied the eigen-face method to sub-images (eye, nose and mouth).

39
They also applied a rotation correction of face image to the faces in order to obtain better results
of face recognition system.
3. DISCUSSION AND REMARKS
In this paper, we presented some major issues on face recognition. These are as follows: Face
detection: For the constrained conditions, many face detection methods for static image are not
directly suitable to the task in video. We classified current approaches into groups, and
summarized their pros and cons. Face tracking: In face tracking head rotation and pose variations
are measure issues. Face tracking is a significant procedure in face recognition. It usually exploits
statistical model, examplar-based model, and skin color information to accomplish the tracking
task. In addition, for these methods it also exploits CAMSHIFT, condensation, adaptive Kalman
filter algorithms. Face recognition: Since the spatio-temporal information plays a significant role
in face recognition, how to fully exploit redundancy information in the video sequence is a key
issue for video based recognition. One of the chief advantages of video over still frames is that
fact accumulation over multiple frames can provide better face recognition performance.
Consequently, face recognition in video possesses more challenges to the current face recognition
systems. Use of three dimensional face image models has been suggested as a way to compensate
for low resolution, low dimension, poor contrast and non-frontal pose. By the way of constructing
a 3D face model from multiple non-frontal frames in a video, and then generating a frontal view
from the derived 3D model, and finally using a 2D face recognition algorithm to recognize the
synthesized frontal view, the spatio-temporal information can be fully employed. Meantime, it
will help solve the problem of occlusion, pose variance and illumination issues caused by video
frame's poor quality.
ACKNOWLEDGEMENT
I wish my sincere thanks to my guide Prof. Dr. P. J. Deore for his continuous support. Also I am
very much thankful to Prof. Dr. J. B. Patil. Finally I would like to thanks my husband Mr. Dinesh
A Patil for his continuous support.
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Authors
Name: Prof. Dr. Pramod. J. Deore
Institute: R. C. Patel Institute of
Technology
City: Shirpur, Tal: Shirpur, Dist:
Dhule, Maharashtra.
Country: India
Email: pjdeore@yahoo.com
Name: Shailaja A. Patil
Institute: R. C. Patel Institute of
Technology
City: Shirpur, Tal: Shirpur, Dist:
Dhule, Maharashtra.
Country: India
Email: shailajadp@yahoo.co.in

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