Facial_recognition_Siva vadapalli1.pptx.ppt

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Introduction to Face
Recognition and Detection

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 Face recognition
 Face recognition processing
 Face detection
 Appearance-based and learning based approaches
 Neural networks methods
 AdaBoost-based methods
 Dealing with head rotations
 Performance evaluation

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Face Recognition by Humans
 Performed routinely and effortlessly by humans
 Enormous interest in automatic processing of digital images and
videos due to wide availability of powerful and low-cost desktop
embedded computing
 Applications:
 biometric authentication,
 surveillance,
 human-computer interaction
 multimedia management

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Face recognition
Advantages over other biometric technologies:
 Natural
 Nonintruisive
 Easy to use
Among the six biometric attributes considered by Hietmeyer, facial
features scored the highest compatibility in a Machine Readable Travel
Documents (MRTD) system based on:
 Enrollment
 Renewal
 Machine requirements
 Public perception

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Classification
A face recognition system is expected to identify faces present in images
and videos automatically. It can operate in either or both of two
modes:
Face verification (or authentication): involves a one-to-one match
that compares a query face image against a template face image
whose identity is being claimed.
Face identification (or recognition): involves one-to-many matches
that compares a query face image against all the template images in
the database to determine the identity of the query face.
First automatic face recognition system was developed by Kanade 1973.

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Face recognition processing
Face recognition is a visual pattern recognition problem.
A face is a three-dimensional object subject to varying
illumination, pose, expression is to be identified based on its
two-dimensional image ( or three- dimensional images obtained
by laser scan).
A face recognition system generally consists of 4 modules -
detection, alignment, feature extraction, and matching.
 Localization and normalization (face detection and alignment)
are processing steps before face recognition (facial feature
extraction and matching) is performed.

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 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, whereas face
detection provides coarse estimates of the location and
scale of each face.
 Facial components and facial outline are located; based
on the location points,
 The input face image is normalized in respect to
geometrical properties, such as size and pose, using
geometrical transforms or morphing,
 The face is further normalized with respect to
photometrical properties such as illumination and gray
scale.

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After a face is normalized, 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.

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Face recognition processing flow.

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Analysis in face subspaces
Subspace analysis techniques for face recognition are based on the fact
that a class of patterns of interest, such as the face, resides in a subspace
of the input image space:
A small image of 64 × 64 having 4096 pixels can express a large number
of pattern classes, such as trees, houses and faces.
Among the 2564096
> 109864
possible “configurations”, only a few
correspond to faces. Therefore, the original image representation is
highly redundant, and the dimensionality of this representation could
be greatly reduced .

With the eigenface or PCA approach, a small number (40 or lower) of
eigenfaces are derived from a set of training face images by using
the Karhunen-Loeve transform or PCA.
A face image is efficiently represented as a feature vector (i.e. a vector
of weights) of low dimensionality.
The features in such subspace provide more salient and richer
information for recognition than the raw image.
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The manifold (i.e. distribution) of all faces accounts for variation in face
appearance whereas the nonface manifold (distribution) accounts for everything else.
If we look into facial manifolds in the image space, we find them highly
nonlinear and nonconvex.
The figure (a) illustrates face versus nonface manifolds and (b) illustrates the
manifolds of two individuals in the entire face manifold.
Face detection is a task of distinguishing between the face and nonface manifolds
in the image (subwindow) space and face recognition between those of
individuals in the face mainifold.
(a) Face versus nonface manifolds. (b) Face manifolds of different individuals.

 The Eigenfaces, Fisherfaces and Laplacianfaces calculated from the
face images in the Yale database.
Examples
Eigenfaces
Fisherfaces
Laplacianfaces

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Technical Challenges
The performance of many state-of-the-art face recognition methods
deteriorates with changes in lighting, pose and other factors. The key
technical challenges are:
Large Variability in Facial Appearance: Whereas shape and
reflectance are intrinsic properties of a face object, the
appearance (i.e. texture) is subject to several other factors,
including the facial pose, illumination, facial expression.
Intrasubject variations in pose, illumination, expression, occlusion,
accessories (e.g. glasses), color and brightness.

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Highly Complex Nonlinear Manifolds: The entire face manifold (distribution) is
highly nonconvex and so is the face manifold of any individual under various
changes. Linear methods such as PCA, independent component analysis (ICA)
and linear discriminant analysis (LDA) project the data linearly from a high-
dimensional space (e.g. the image space) to a low-dimensional subspace. As
such, they are unable to preserve the nonconvex variations of face manifolds
necessary to differentiate among individuals.
In a linear subspace, Euclidean distance and Mahalanobis distance do not
perform well for classifying between face and nonface manifolds and between
manifolds of individuals. This limits the power of the linear methods to
achieve highly accurate face detection and recognition.

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High Dimensionality and Small Sample Size: Another challenge is the ability
to generalize as illustrated in figure. A canonical face image of 112 × 92
resides in a 10,304-dimensional feature space. Nevertheless, the number of
examples per person (typically fewer than 10) available for learning the
manifold is usually much smaller than the dimensionality of the image
space; a system trained on so few examples may not generalize well to
unseen instances of the face.

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Technical Solutions
 Feature extraction: construct a “good” feature space in which the
face manifolds become simpler i.e. less nonlinear and nonconvex
than those in the other spaces. This includes two levels of
processing:
Normalize face images geometrically and photometrically, such as
using morphing and histogram equalization
Extract features in the normalized images which are stable with respect
to such variations, such as based on Gabor wavelets.
 Pattern classification: construct classification engines able to solve
difficult nonlinear classification and regression problems in the
feature space and to generalize better.

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Technical Solutions
Learning-based approach - statistical learning
 Learns from training data to extract good features and construct
classification engines.
 During the learning, both prior knowledge about face(s) and
variations seen in the training data are taken into consideration.
 The appearance-based approach such as PCA and LDA based
methods, has significantly advanced face recognition techniques.
 They operate directly on an image-based representation (i.e. an array
of pixel intensities) and extracts features in a subspace derived from
training images.

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Technical Solutions
Appearance-based approach utilizing
geometric features
Detects facial features such as eyes, nose, mouth and chin.
- Detects properties of and relations (e.g. areas, distances,
angles) between the features are used as descriptors for face
recognition.
Advantages:
 economy and efficiency when achieving data reduction and
insensitivity to variations in illumination and viewpoint
 facial feature detection and measurement techniques are not
reliable enough is they are based on the geometric feature
based recognition only
 rich information contained in the facial texture or appearance is
still utilized in appearance-based approach.

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Technical Solutions
Nonlinear kernel techniques
Linear methods can be extended using nonlinear
kernel techniques (kernel PCA and kernel LDA) to deal
with nonlinearly in face recognition.
 A non-linear projection (dimension reduction) from the
image space to a feature space is performed; the manifolds in
the resulting feature space become simple, yet with subtleties
preserved.
 A local appearance-based feature space uses appropriate
image filters, so the distributions of faces are less affected by
various changes. Examples:
 Local feature analysis (LFA)
 Gabor wavelet-based features such as elastic graph bunch
matching (EGBM)
 Local binary pattern (LBP)

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Taxonomy of face recognition algorithms
Taxonomy of face recognition algorithms based on pose-dependency,
face representation, and features used in matching.

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Appearance-based and learning based
approaches
Face detection is treated as a problem of classifying each
scanned subwindow as one of two classes (i.e. face
and nonface).
Appearance-based methods avoid difficulties in modeling
3D structures of faces by considering possible face
appearances under various conditions.
A face/nonface classifier may be learned from a training
set composed of face examples taken under possible
conditions as would be seen in the running stage and
nonface examples as well.
Disadvantage: large variations brought about by
changes in facial appearance, lighting and expression
make the face manifold or face/non-face boundaries
highly complex.

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Neural Networks and Kernel Based Methods
Nonlinear classification for face detection may be performed using
neural networks or kernel-based methods.
Neural methods: a classifier may be trained directly using
preprocessed and normalized face and nonface training
subwindows.
 The input to the system of Sung and Poggio is derived from the six
face and six nonface clusters. More specifically, it is a vector of 2 × 6
= 12 distances in the PCA subspaces and 2 × 6 = 12 distances from
the PCA subspaces.
 The 24 dimensional feature vector provides a good representation
for classifying face and nonface patterns.
 In both systems, the neural networks are trained by back-
propagation algorithms.
Kernel SVM classifiers perform nonlinear classification for face
detection using face and nonface examples.
 Although such methods are able to learn nonlinear boundaries, a
large number of support vectors may be needed to capture a highly
nonlinear boundary. For this reason, fast realtime performance has
so far been a difficulty with SVM classifiers thus trained.

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AdaBoost-based Methods
Haar-like features
Viola and Jones propose four basic types of scalar features for face detection as
shown in figure. Such a block feature is located in a subregion of a subwindow
and varies in shape (aspect ratio), size and location inside the subwindow.
For a subwindow of size 20 × 20, there can be tens of thousands of such features
for varying shapes, sizes and locations. Feature k, taking a scalar value zk(x) Є R,
can be considered a transform from the n-dimensional space to the real line.
These scalar numbers form an overcomplete feature set for the intrinsically low-
dimensional face pattern.
Recently, extended sets of such features have been proposed for dealing with out-of-
plan head rotation and for in-plane head rotation.
These Haar-like features are interesting for two reasons:
powerful face/nonface classifiers can be constructed based on these features
they can be computed efficiently using the summed-area table or integral image
technique.
Four types of rectangular Haar wavelet-like
features. A feature is a scalar calculated by
summing up the pixels in the white region and
subtracting those in the dark region.

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Constructing weak classifiers
The AdaBoost learning procedure is aimed at learning a sequence
of best weak classifiers to combine hm(x) and the combining
weights αm. It solves the following three fundamental problems:
Learning effective features from a large feature set
Constructing weak classifiers, each of which is based on one of
the selected features
Boosting the weak classifiers to construct a strong classifier

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Constructing weak classifiers (cont’d)
AdaBoost assumes that a “weak learner” procedure is available.
The task of the procedure is to select the most significant feature from a set of
candidate features, given the current strong classifier learned thus far, and
then construct the best weak classifier and combine it into the existing
strong classifier.
In the case of discrete AdaBoost, the simplest type of weak classifiers is a
“stump”. A stump is a single-node decision tree. When the feature is real-
valued, a stump may be constructed by thresholding the value of the
selected feature at a certain threshold value; when the feature is discrete-
valued, it may be obtained according to the discrete label of the feature.
A more general decision tree (with more than one node) composed of several
stumps leads to a more sophisticated weak classifier.

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Boosted strong classifier
 AdaBoost learns a sequence of weak classifiers hm and boosts
them into a strong one HM effectively by minimizing the upper
bound on classification error achieved by HM. The bound can
be derived as the following exponential loss function:
where i is the index for training examples.

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AdaBoost learning algorithm
AdaBoost learning algorithm

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FloatBoost Learning
AdaBoost attempts to boost the accuracy of an ensemble of weak classifiers. The
AdaBoost algorithm solves many of the practical difficulties of earlier boosting
algorithms. Each weak classifier is trained stage-wise to minimize the
empirical error for a given distribution reweighted according to the
classification errors of the previously trained classifiers. It is shown that
AdaBoost is a sequential forward search procedure using the greedy selection
strategy to minimize a certain margin on the training set.
A crucial heuristic assumption used in such a sequential forward search
procedure is the monotonicity (i.e. that addition of a new weak classifier to
the current set does not decrease the value of the performance criterion).
The premise offered by the sequential procedure in AdaBoost breaks down
when this assumption is violated.
Floating Search is a sequential feature selection procedure with backtracking,
aimed to deal with nonmonotonic criterion functions for feature selection. A
straight sequential selection method such as sequential forward search or
sequential backward search adds or deletes one feature at a time. To make
this work well, the monotonicity property has to be satisfied by the
performance criterion function. Feature selection with a nonmonotonic
criterion may be dealt with using a more sophisticated technique, called plus-
L-minus-r, which adds or deletes L features and then backtracks r steps.

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The FloatBoost Learning procedure is
composed of several parts:
 the training input,
 initialization,
 forward inclusion,
 conditional exclusion and
 output.
In forward inclusion, the currently most
significant weak classifiers are
added one at a time, which is the
same as in AdaBoost.
In conditional exclusion, FloatBoost
removes the least significant weak
classifier from the set HM of current
weak classifiers, subject to the
condition that the removal leads to
a lower cost than Jmin
M-1. Supposing
that the weak classifier removed
was the m’-th in HM, then hm’,…,hM-1
and the αm’s must be relearned.
These steps are repeated until no
more removals can be done.
FloatBoost algorithm
FloatBoost
algorithm

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 Cascade of Strong Classifiers: A boosted strong classifier
effectively eliminates a large portion of nonface
subwindows while maintaining a high detection rate.
Nonetheless, a single strong classifier may not meet the
requirement of an extremely low false alarm rate (e.g. 10-6
or even lower). A solution is to arbitrate between several
detectors (strong classifier), for example, using the “AND”
operation.
A cascade of n strong classifiers (SC). The input is a subwindow x. It is sent to
the next SC for further classification only if it has passed all the previous SCs
as the face (F) pattern; otherwise it exists as nonface (N). x is finally
considered to be a face when it passes all the n SCs.

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Dealing with Head Rotations
Multiview face detection should be able to detect nonfrontal faces. There
are three types of head rotation:
out-of-plane rotation (look to the left – to the right)
in-plane rotation (tilted toward shoulders)
up-and-down nodding rotation (up-down)
Adopting a coarse-to-fine view-partition strategy, the detector-pyramid
architecture consists of several levels from the coarse top level to the
fine Bottom level.
Rowley et al. propose to use two neural network classifiers for detection of
frontal faces subject to in-plane rotation.
 The first is the router network, trained to estimate the orientation of an
assumed face in the subwindow, though the window may contain a
nonface pattern. The inputs to the network are the intensity values in a
preprocessed 20 × 20 subwindow. The angle of rotation is represented
by an array of 36 output units, in which each unit represents an angular
range.
 The second neural network is a normal frontal, upright face detector.

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Coarse-to-fine:The partitions of the out-of-plane rotation for the
three-level detector-pyramid is illustrated in figure.
Out-of-plane view partition. Out-of-plane head rotation (row 1), the
facial view labels (row 2), and the coarse-to-fine view partitions at the
three levels of the detector-pyramid (rows 3 to 5).

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Simple-to-complex: A large number of subwindows result
from the scan of the input image. For example, there can
be tens to hundreds of thousands of them for an image of
size 320 × 240, the actual number depending on how the
image is scanned.
Merging from different channels. From left to right: Outputs of frontal, left and
right view channels and the final result after the merge.

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Outline
 Face recognition
 Face recognition processing
 Analysis in face subspaces
 Technical challenges
 Technical solutions
 Face detection
 Appearance-based and learning based approaches
 Neural networks and kernel-based methods
 AdaBoost-based methods
 Dealing with head rotations
 Performance evaluation

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Performance Evaluation
The result of face detection from an image is affected
by the two basic components:
 the face/nonface classifier: consists of face icons
of a fixed size (as are used for training). This process
aims to evaluate the performance of the
face/nonface classifier (preprocessing included),
without being affected by merging.
 the postprocessing (merger): consists of normal
images. In this case, the face detection results are
affected by both trained classifier and merging; the
overall system performance is evaluated.

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Performance Measures
 The face detection performance is primarily measured by two
rates: the correct detection rate (which is 1 minus the miss
detection rate) and the false alarm rate.
 As AdaBoost-based methods (with local Haar wavelet features)
have so far provided the best face detection solutions in terms
of the statistical rates and the speed
 There are a number of variants of boosting algorithms: DAB-
discrete Adaboost; RAB- real Adaboost; and GAB- gentle
Adaboost, with different training sets and weak classifiers.
 Three 20-stage cascade classifiers were trained with DAB, RAB
and GAB using the Haar-like feature set of Viola and Jones and
stumps as the weak classifiers. It is reported that GAB
outperformed the other two boosting algorithms; for instance,
at an absolute false alarm rate of 10 on the CMU test set, RAB
detected only 75.4% and DAB only 79.5% of all frontal faces,
and GAB achieved 82.7% at a rescale factor of 1.1.

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 Two face detection systems were trained: one with the basic
Haar-like feature set of Viola and Jones and one with the
extended Haar-like feature set in which rotated versions of the
basic Haar features are added.
 On average, the false alarm rate was about 10% lower for the
extended haar-like feature set at comparable hit rates.
 At the same time, the computational complexity was
comparable.
 This suggests that whereas the larger haar-like feature set
makes it more complex in both time and memory in the
boosting learning phase, gain is obtained in the detection
phase.

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Regarding the AdaBoost approach, the following conclusions can be drawn:
 An over-complete set of Haar-like features are effective for face detection. The use of
the integral image method makes the computation of these features efficient and
achieves scale invariance. Extended Haar-like features help detect nonfrontal faces.
 Adaboost learning can select best subset from a large feature set and construct a
powerful nonlinear classifier.
 The cascade structure significantly improves the detection speed and effectively
reduces false alarms, with a little sacrifice of the detection rate.
 FloatBoost effectively improves boosting learning result. It results in a classifier that
needs fewer weaker classifiers than the one obtained using AdaBoost to achieve a
similar error rate, or achieve a lower error rate with the same number of weak
classifiers. This run time improvement is obtained at the cost of longer training time.
 Less aggressive versions of Adaboost, such as GentleBoost and LogitBoost may be
preferable to discrete and real Adaboost in dealing with training data containing
outliers (distinct, unusual cases).
 More complex weak classifiers (such as small trees) can model second-order and/or
third-order dependencies, and may be beneficial for the nonlinear task of face
detection.

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References
 S. Z. Li and A. K. Jain. Handbook of Face recognition, 2005
 R. Hietmeyer. Biometric identification promises fast and secure processing of airline
passengers. The Intl. Civil Aviation Organization Journal, 2000.
 Machine Readable Travel Document (MRTD)
http://www.icao.int/mrtd/Home/Index.cfm
 T. Kanade. Picture processing by computer complex and recognition of human faces.
Ph.D. thesis, 1973
 K. Fukunaga. Introduction to statistical pattern recognition. Academic Press, 1990
 M. Bichsel and A. P. Pentland. Human face recognition sand the face image set’s
topology. CVGIP, 1994
 M. Turk. A random walk through eigenspace. IEICE Tans. Inf. & Syst., 2001
 M. A. Turk and A. P. Pentland. Eigenfaces for recognition. Journal of Cognitive
Neuroscience, 1991.
 Peter N. Belhumeur, Joao P. Hespanha, and David J. Kriegman. Eigenfaces vs.
Fisherfaces: Recognition. Using Class Specific Linear Projection, IEEE Transactions on
Pattern Analysis and Machine Intelligence, 1997
 Face recognition Vendor Tests www.frvt.org.
 E. Hjelmas and B. K. Low Face detection: A survey. Computer Vision and Image
Understanding., 2001
 M.-H. Yang, D. Kriegman, and N. Ahuja. Detecting faces in images: a survey. IEEE
Trans. On Pattern Analysis and Machine Intelligence, 2002

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