Facial Recognition System

Facial Recognition System
CHAPTER 1 FACIAL RECOGNITION SYSTEM
1.1 INTRODUCTION
A biometrics is, "Automated methods of recognizing an individual based on their unique physical or
behavioral characteristics." For example face, fingerprint, signature, voice etc. Face recognition is a
task humans perform remarkably easily and successfully.
In face recognition Features extracted from a face are processed and compared with similarly
processed faces present in the database. If a face is recognized it is known or the system may show a
similar face existing in database else it is unknown.
Face recognition has been a topic of active research since the 80's, proposing solutions to several
practical problems. It has been a challenging job for the researchers to develop a facial recognition
system with 100% accuracy because of all the difficulties and limitations. As we know that the
human face changes after a short period of time so no facial recognition system can work perfectly
after a few years. It has to be updated with the latest database images of the person in order to verify
the person. Also wearing spectacles, mask, mustaches may also affect the output of the face
recognition system. Face recognition is a biometric which is much easier to understand, because we
recognize or identify different people mostly by their faces. However the recognition process used
by the human brain for identifying faces has not a concrete explanation. It has now become essential
to have reliable security systems in
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Solving The Physics Of The Problem
As the name suggests, there are no basic guidelines for these algorithms, hence it is unsupervised.
These algorithms can be used to discover various pattern, divide the data into various clusters,
reducing the dimensionality of the dataset for viewing, which may help researchers in better
understanding of the physics of the problem. Here, an expert needs to be careful while choosing a
certain algorithm and associated parameters for a specific case. Additionally, an expert needs to be
very careful while interpreting the findings from these algorithms. One must use the technical
aspects regarding the basic physics of the problem so that their results are meaningful and for it to
be accepted by the materials research specialists for ... Show more content on Helpwriting.net ...
The final result is a tree like structure referred as Dendrogram, which shows the way the clusters are
related. User can specify a distance or number of clusters to view the dataset in disjoint groups. In
this way, the user can get rid of a cluster that does not serve any purpose as per his expertise. In this
case, we used MVA (Multivariate data analysis) node in optimization package: modeFRONTIER
(ESTECO, 2015) and other statistical software IBM SPSS (IBMSPSS, 2015) for HCA analysis.
Clusters are classified by following measures (ESTECO, 2015) 1. Internal similarity (ISim): It
reflects the compactness of the k–th cluster. It must be higher. 2. External similarity (ESim): It
reflects the uniqueness of the k – th cluster. It must be lower. 3. Descriptive variables: are the most
significant variables that help in identifying cluster elements that are similar to one another. 4.
Discriminating variables: are the most significant variables that help in identifying cluster elements
that are dissimilar to other clusters. HCA analysis can be used to cross check the findings of SVR
analysis mentioned above in the text.
4.3.2 Principal Component Analysis (PCA) Principal component analysis can be classified as an
unsupervised learning machine–learning algorithm [Mueller et~al., 2015]. It was performed in order
to determine correlations

Brief Explanation of the Basic Framework of the Principal...
PRELIMINARIES
This section expands a brief explanation of the basic frame–work of the principal component
analysis and fuzzy logic, along with some of the key basic concepts.
A. The principal component analysis (PCA)
The Principal component analysis (PCA) is an essential technique in data compression and feature
reduction [13] and it is a statistical technique applied to reduce a set of correlated variables to
smaller uncorrelated variables to each other. PCA is considered as special transformation which
produces the principal components (PCs) Known as eigenvectors. PCs are sorted decreasing i.e. the
first prin–cipal component (PC
1
) has largest of the variance. That is meanvar(PC1 );var(PC2
);var(PC3
);var(PCp
), where var(PCi ) ... Show more content on Helpwriting.net ...
PCA is considered as special transformation which pro–duces the principal components (PCs)
Known as eigenvectors
.PCs are sorted decreasing i.e. the first principal component
(PC
1) has largest of the variance; That is mean var (PC
1
) var (PC
2) var (PC
3) ... var (PC
p), where var(PC i ) expresses the variance of(PC i ) [14]. The PCA has characteristics and ability to
reduce redundancy and uncertainty. So this paper used the PCA as the preprocessing step on the
multispectral images to reduce the redundancy information and focus on the component; that have a
significant impact on the data.
B. Fuzzy logic
Zadeh [15] introduced the concept of fuzzy logic to present vagueness in linguistics, and to
implement and express human knowledge and inference capability in a natural way. The fuzzy logic
starts with the concept of fuzzy sets which is defined as a set without a crisp. It is clearly defined
boundary and can contain elements with only a partial degree of member–ship.The main power of
fuzzy logic image processing is in the middle step (membership function) [16] A membership
function defines how each value in the input space is mapped to a membership value (or degree of
membership) between the range of 0 and 1. LetXbe the input space andxbe a generic element ofX. A

classical set Ais defined as a collection of elements or objectsx2X, such that each xcan either belong
or not belong to the setA, AvX.

Face Recognition Using Orthogonal Locality Preserving...
FACE RECOGNITION USING ORTHOGONAL LOCALITY PRESERVING PROJECTIONS.
Dr. Ravish R Singh Ronak K Khandelwal Manoj Chavan
Academic Advisor EXTC Engineering EXTC Engineering Thakur Educational Trust L.R.Tiwari
COE Thakur COE
Mumbai, India. Mumbai,India. Mumbai, India. ravishrsingh@yahoo.com
ronakkhandelwal2804@gmail.com prof.manoj@gmail.com
Abstract: In this paper a hybrid technique is used for determining the face from an image. Face
detection is one of the tedious job to achieve with very high accuracy. In this paper we proposed a
method that combines two techniques that is Orthogonal Laplacianface (OLPP) and Particle Swarm
Optimization (PSO). The formula for the OLPP relies on the Locality Preserving Projection (LPP)
formula, which aims at ï¬nding a linear approximation to the Eigen functions of the astronomer
Beltrami operator on the face manifold. However, LPP is non–orthogonal and this makes it difficult
to reconstruct the information. When the set of features is found by the OLPP, with the help of the
PSO, the grouping of the image features is done and the one with the best match from the database
is given as the result. This hybrid technique gives a higher accuracy in less processing time.
Keywords: OLPP, PSO,
INTRODUCTION:
Recently, appearance–based face recognition has received tons of attention. In general, a face image
of size n1 Ã— n2 is delineating as a vector within the image house Rn1 Ã— n2. We have a
tendency to denote

Techniques Used For The Face Recognition
2.Literature Survey
There are so many techniques used for the face recognition in all over the world. It plays a very
important role in our daily lives nowadays as it protect us from thieves, used for our identity etc.
Some of the techniques for face recognition includes eigenfaces, principle component analysis,
independent component analysis, elastic bunch graph machine, range imaging, gabor wavelet
networks and many more.
We will focus on some human face recognition techniques using neural networks those are mostly
applicable to the frontal faces, and its advantages and disadvantages in this part of the report.
The linearity in the network of neural network is the most attention seeking cause of using it. A
single layer adaptive network was firstly used for the face recognition which is also known as
WISARD. For each provided individual, it has a single network. For an effective recognition, the
construction of the neural network structure is very important which are based on the applications
we had studied before. Different types of neural network is used for different techniques, for
example, multilayer perceptron shown in fig.2.1 is used for the detection of the face and multi–
resolution pyramid structure shown in fig.2.2 is used for the verification of the face. fig.2.1.
Multilayer Perceptron fig.2.2. Multi–resolution Pyramid Structure
Steve Lawrence and Andrew D. Back in 1997 referred to [1] proposed a convolutional neural
network approach which includes the

Face Recognition Essay
SECURITY SURVEILLANCE SYSTEM
Security Through Image Processing
Prof.Vishal Meshram,Jayendra More
Department of Electronics Telecommunication Engineering (ExTC), University Of Mumbai
Vishwatmak Om Gurudev College Of Engineering Maharashtra State Highway 79, Mohili,
Maharashtra 421601, India.
Vishalmmeshram19@gmail.com, more.jayendra@yahoo.in
Bhagyesh Birari,Swapnil Mahajan
Department of Electronics Telecommunication Engineering (ExTC), University Of Mumbai
Vishwatmak Om Gurudev College Of Engineering Maharashtra State Highway 79, Mohili,
Maharashtra 421601, India. bhagyeshbb86@gmail.com, swapnilmahajan939@gmail.com
Abstract–Automatic recognition of people is a challenging problem which has received much
attention during recent years due to its many applications in different fields. Face recognition is one
of those challenging problems and up to date, there is no technique that provides a robust solution to
all situations. This paper presents a technique for human face recognition. A self–organizing
program is used to identify if the subject in the input image is present or not present in the image
database. Face recognition with Eigen values is carried out by classifying Eigen values in both
images. The main advantage of this technique is its high–speed processing capability and low
computational requirements, in terms of both speed, accuracy and memory utilization. The goal is to
implement the system for a particular face and distinguish it

V. Particle Swarm Optimization ( Pso )
V. Particle Swarm optimization (PSO): It is a swarm–based intelligence algorithm influenced by the
social behavior of animals cherishes a flock of birds finding a food supply or a school of fish
protecting themselves from a predator. A particle in PSO is analogous to a bird or fish flying through
a search (problem) area. The movement of every particle is coordinated by a rate that has each
magnitude and direction. Every particle position at any instance of your time is influenced by its
best position and also the position of the most effective particle in an exceedingly drawback area.
The performance of a particle is measured by a fitness worth that is drawback specific. The PSO
rule is analogous to different biological process algorithms. In PSO, the population is that the range
of particles in a drawback area. Particles square measure initialized arbitrarily. Each particle can
have a fitness worth, which is able to be evaluated by a fitness perform to be optimized in every
generation. Each particle is aware of its best position pbest and also the best position so far among
the whole cluster of particles gbest. The pbest of a particle is that the best result (fitness value) to
date reached by the particle, whereas gbest is that the best particle in terms of fitness in a whole
population. Algorithm 2 PSO algorithm: 1. Set particle dimension as equal to the size of ready tasks
in {ti) â‚¬ T 2. Initialize particles position randomly from PC = 1,....,j and velocity vi, randomly.

Digital Image Processing : A Multi Dimensional Visual...
ABSTRACT:
Face is a analyzable multi–dimensional visual model and processing a process model for face
recognition is challenging. This paper presents a methodological analysis for face identification
based on content explanation formulation of coding and decoding the face image. categorization
using the Euclidian distance. The content is to use the system for a particular face and separate from
a large number of stored faces with some real time variations as well. The Eigen face attack uses
particular faces with some real time variation. The Eigen face formulation uses principal
components analysis (PCA) algorithm for the acceptance of the images. It gives us prompt way to
insight the lower dimensional space.
Digital Image processing: ... Show more content on Helpwriting.net ...
The sampling theorem states that for a signal to be completely reconstruct able, it must satisfy the
following equation:
Were Ws=sampling frequency W = frequency of sampled signal
. To explain all of this, first consider the simple sinusoidal function given by f(x) = cos(x). Figure 1
shows a plot of this function and Fig. 2 shows a plot of its Fourier transform.
Figure 3 shows a truncated version of that function, and Fig.4 shows the equivalent Fourier
transform.
Figure 1. Cosine function with amplitude A and frequency of 1 Hz.
Figure 2. Power spectrum of the cosine function with amplitude A and frequency of 1 Hz. Figure 3.
Truncated cosine function. The truncation is in the variable x (e.g., time), not in the amplitude.
Figure 4. The power spectrum of the truncate cosine function is a continuous one, with maximum
values at the same points, like the power spectrum of the continuous cosine function.
This is called as folding. In the above fig4 shows that lower frequencies of signal contains most of
signal's powers. A standard analog filter transfer function may be given as
Where ï¸the damping factor of the filter and w is is its natural frequency. By cascading first and
second order filters, one of them will get higher order systems which have higher performances.
Bessel filters are used for high performance applications, this is because of two factors.
1) The damping factors

Image Fusion Technique Based on PCA and Fuzzy Logic Essay
This paper presents a image fusion technique based on PCA and fuzzy logic. the framework of the
proposed image fusion technique is divided in the following major phases:
preprocesing phase
Feature extraction based on the principal component analysis The image fusion based on fuzzy
set
Reconstruction final image
The figure (1) shows the framework of the proposed image fusion and its phases.
Fig. 1. The proposed approach of image fusion phases
A. Preprocessing Phase
This phase consists of three steps registration , resampling and histogram matching .In the following
1) Registration:Image fusion is the approach of combining two or more images of same scene to
obtain the more informative image. The image data is recorded by sensors
on ... Show more content on Helpwriting.net ...
Bilinear resampling is known also bilinear filtering or bilinear interpolation. Bilinear resampling is
used to smooth out when they are displayed smaller or larger than they actually are. the bilinear
resampling is done by interpolating between the four pixels nearest to the point that best represents
that pixel
(usually in the middle or upper left of the pixel). The bilinear resampling takes a weighted average
of 4 pixels in the original image nearest to the new pixel location The averaging process modifies
the original pixel values and creates entirely new digital values in the output image. Bilinear
resampling results are smoother,accurate,without stairstepped effect. But it has some limitations that
is edges are smoothed and some extremes of the data file values are lost. It is expressed
mathematically as follows Assuming i and j are integer parts of x and y, respectively; bilinear
resampling is defined by:
F(x;y) =Wi;j[F(i;j)] +Wi+ 1;j[F(i+ 1;j)]
+Wi;j+ 1[F(i;j + 1)] +Wi+ 1;j+ 1[F(i+ 1;j+ 1)]
(8)
where
Wi;j= (i+ 1 x)(j+ 1 y)
Wi+ 1;j= (x i)(j+ 1 y)
Wi;j+ 1 = (i+ 1 x)(y j)
Wi+ 1;j+ 1 = (x i)(y j)
3) Histogram Matching: As previously mentioned Image fusion is the approach of combining two or
more images of same scene to obtain the more informative image. The histogram matching is

important step in the preprocessing for the image fusion.The histogram of an image illustrates the
frequency of

Advantages And Disadvantages Of Eye And Face Recognition
Sudeep Sarkar et.al.[10] Researchers have suggested that the ear may have advantages over the face
for biometric recognition. Our previous experiments with ear and face recognition, using the
standard principal component analysis approach, showed lower recognition performance using ear
images. We report results of similar experiments on larger data sets that are more rigorously
controlled for relative quality of face and ear images. We find that recognition performance is not
significantly different between the face and the ear.
Haitao Zhao, Pong Chi Yuen says face recognition has been an active research area in the computer–
vision and pattern–recognition societies [11] in the last two decades. Since the original input–image
space has a very high dimension, a dimensionality–reduction technique is usually employed before
classification takes place. Principal component analysis (PCA) is one of the most popular
representation methods for face recognition. It does not only reduce the image dimension, but also
provides a compact feature for representing a face image. In 1997, PCA was also employed for
dimension reduction for linear discriminant . PCA is ... Show more content on Helpwriting.net ...
The images forming the training set (database) are projected onto the major eigenvectors and the
projection values are computed. In the recognition stage the projection value of the input image is
also found and the distance from the known projection values is calculated to identify who the
individual is.Neural Network Based Face Recognition procedure is followed for forming the
eigenvectors as in the Eigenface approach, which are then fed into the Neural Network Unit to train
it on those vectors and the knowledge gained from the training phase is subsequently used for
recognizing new input images. The training and recognition phases can be implemented using
several neural network models and

Problems With Battling Malware Have Been Discussed, Moving...
Now that issues with battling malware have been discussed, moving to solutions is the next step.
Utilizing deobfuscation, especially through signature analysis, has already been discussed to its
fullest potential. New methods include CPU analyzers, holograpy, eigenvirus detection, differential
fault analysis, the growing grapes method, and whitelist protection. These are more general
approaches and therefore do not rely on storing certain specific characteristics of the code of
malware and tend to analyze behavior. Due to the extreme focus on deobfuscation, these ideas have
only been explored fairly recently and are currently underdeveloped. As was stated in the previous
section, CPU analyzers are a possible valid method of detecting malware. While it can be unreliable
alone, O 'Kane et al. believe it can be a good preliminary detection method for metamorphic
malware due to high CPU processing times (2011). The main issue is valid processes may trigger a
warning with this type of detection. This is why it must be paired with another detection method. A
newer study examines a type of anti–malware called holography. Dai, Fyodor, Wu Huang, and Kuo,
researchers at the National Taiwan University and the Research Center for Information Technology
Innovation in Taipei, state that holography utilizes CPU analysis and memory instructions in order
to analyze malware and detect infections (2012). However, this method is, in general, more useful
currently as an analysis method

What Are The Advantages And Disadvantages Of Biometrics
There are many biometric techniques in existence today. Face recognition technology is one of them
which make use of computer software to determine the identity of the person. Today conservative
methods of identification like possession of certain type of identity cards, use of passwords etc. are
not at all reliable for identity purposes where security is a critical factor. There is no surety in the
fact that person using ATM card to withdraw money from any ATM machine is actual owner of the
card. When credit and ATM cards are lost or stolen, it is not a big game for the unauthorized user to
make an accurate guess of the correct personal codes. It is a common practice between we people
that despite of strict warning we continue to choose easily guessed PIN's and passwords. Often we
prefer our birthdays, cell numbers, house numbers and vehicle numbers. Identity cards can be lost,
fake or misplaced and passwords can be forgotten or compromised. But a face is unquestionably
connected to its owner. Face does not suffer the limitations of been borrowed, stolen or easily
copied. Face recognition technology is the fastest and least intrusive biometric technology. Human
face is one such part of the human body that can help ... Show more content on Helpwriting.net ...
Reduced fraud – It becomes extremely difficult for somebody to willingly give up his or her
biometric data, so sharing identities is virtually impossible. In addition, because it becomes
necessary to expose one's own biometric data (i.e. your own face), potential fraudsters are reluctant
to attempt false verification. Cost reduction. By replacing plastic swipe cards, all cost associated
with producing, distributing and replacing a lost card is completely

Biometrics : Biometrics And Biometrics
1. Introduction
Biometrics is a method of identifying an individual based on characteristics that they possess,
typically physiological features such as a fingerprint, hand, iris, retina, face, voice, and even DNA.
Some methods of biometrics security even use multiple physiological features or multimodal
biometrics to provide superior security than a single form of biometrics can provide. Why are
biometrics important in the field of information security? Biometrics provide a remarkable amount
of security for information because biometrics are unique to each person, and thus cannot be lost,
copied, or shared with another individual. This security allows for biometrics to provide a means to
reliability authenticate personnel. The importance of biometrics can be further divided into the
history of biometrics and why it was devised, past implementations of biometrics, current
implementations of biometrics, and future implementations of biometrics.
2. Importance of Biometrics
Biometrics are important to not only information systems, but to information security as a subject.
Today, most information is kept secure via ID cards or secret information, such as a PIN, password,
pattern, etc., the downside to this type of security is the lack of a failsafe (Ashok, Shivashankar and
Mudiraj)! What would happen if an ID card was lost? Or a PIN, password or pattern was leaked to
individuals who were not on a need to know basis? This is where the importance of biometrics
comes into play.

Location Based Sentiment Analysis Of Twitter Data: A...
Location based sentiment analysis of Twitter data: A Literature Review I.Karthika1,
S.Priyadharshini2 1Assistant Professor, Department of Computer Science and Engineering,
M.Kumarasamy College of Engineering, Karur. 2PG scholar, Department of Computer Science and
Engineering, M.Kumarasamy college of Engineering, Karur.
2priyadharshinisivasamy93@gmail.com Abstract Big data is a concept used for collecting, storing,
and analyzing large volume of data and provides decision making and also support optimization
processes. Social media plays an important role in taking decisions about any products based on the
reviews provided by the user. It accurately tells about the exact opinion of the user regarding the
product. Twitter is one ... Show more content on Helpwriting.net ...
The next phase is the data preprocessing which involves the filtering of the tweets with proper
grammatical relation. Sentiment score phase involves the scoring process. Once analysis process is
completed, the comparisons are made based on location, feature and gender. II LITERATURE
REVIEW Syed akib anwar et al. [1] proposed that Public sentiments are the main things to be
noticed for collecting the feedback of the product. It can be done by using sentiment analysis. The
twitter is the social media used in this paper for collecting the reviews about any product. The
reviews collected are analyzed based on the locations, features and gender. There are four steps
involved in the paper: Data extraction which involves collecting the twitter data, data processing
involves filtering out the redundant tweets and non grammatical relations, implementation involving
the product analysis using sentiment score and result involves comparison between gender, feature
and locations. Xing Fang et al. [2] discusses that Sentiment analysis is a technique used for
categorization of the product based on the reviews of the user. The categories of the product are
good, bad or neutral. In this paper, the general problem of the sentiment polarity categorization has
been resolved. The sentiment polarity categorization consists of two phases: sentence level
categorization and review level categorization. The sentence

Technique Description Performance Evaluation Matrices
Technique Description Performance Evaluation Matrices Reference
Markovian stochastic mixture approach It is composed of three main sections which includes face
detection, face alignment and face recognition. Usually these sections are executed in bottom up
approach. CSU Face Identification Evaluation System is used to evaluate the performance of the
technique and it is found that bottom up approach proposed has better identification rate tested on
104 images. [9] orthogonal locality preserving projection (OLPP) method Novel face recognition
method based on projections of high–dimensional face image representations into lower
dimensionality and highly discriminative spaces. Tested for a number of datasets and it is found that
accuracy of image recognition for proposed approach is higher. The results are evaluated for
different resolutions of the image and it is found that even for low resolution accuracy is
considerably more for proposed approach. [11]
Multi–algorithmic approach It is combination of Principal component analysis (PCA), Discrete
cosine transform (DCT), Template matching using correlation (Corr) and partitioned iterative
function system (PIFS). All the hybrid approaches have been evaluated using accuracy i.e
recognition rate. It is found recognition rate is higher for combination of all four approaches.
Correlation function is used to calculate recognition rate. [12]
KNN approach based on LPP First the vectors of source faces and target faces into feature

In this paper we present an analysis of face recognition...
In this paper we present an analysis of face recognition system with a combination of Neural
networks withSub–space method of feature extraction. Here we are considering both single layer
such as Generalized Regression neural network (GRNN) and Multi layer such as Learning Vector
quantization (LVQ). The analysis of these neural networks are done between feature vectors with
respect to the recognition performance of subspace methods, namely Principal Component Analysis
(PCA) and Fisher Linear Discriminant Analysis (FLDA).Subspace is a multiplex embedded in a
higher dimensional vector space and extracting important features from the damn dimensionality.
The experiments were performed using standard ORL, Yale and FERET database. From the ... Show
more content on Helpwriting.net ...
In section IV, Experimental results are discussed and analysis is briefed. Finally, Conclusions are
drawn.
II. PROPOSED METHOD
In this section an overview of different subspaces methods such as PCA and FLDA are describes in
detail.
A. Principal Component Analysis
PCA is a classical feature extraction and data representation technique also known as Karhunen–
Loeve Expansion [20, 21]. It is a linear method that projects the high–dimensional data onto a lower
dimensional space. It seeks a weight projection that best represents the data, which is called
principal components. Fig.1 Schematic illustration of PCA
Principal component analysis seeks a space of lower dimensionality, known as the principal
subspace and denoted by the magenta line, such that the orthogonal projection of the data points (red
dots) onto this subspace maximizes the variance of the projected points (green dots). An alternative
definition of PCA is based on minimizing the sum–of–squares of the projection errors, indicated by
the blue lines as described in figure 1.
PCA is described as let a face image be A(x, y) be a two–dimensional N by Narray. The training set
images are mapped onto a collection of vector points in this huge space, these vector points are
represented as subspace. These vector points are the eigen vectors which is obtained from the
covariance matrix which defines the subspace of face images.Let the

ISC CISSP Practice Test
ISC CISSP
ISC CISSP Certified Information Systems Security Professional
Practice Test
Version
ISC CISSP: Practice Exam QUESTION NO: 1 All of the following are basic components of a
security policy EXCEPT the A. definition of the issue and statement of relevant terms. B. statement
of roles and responsibilities C. statement of applicability and compliance requirements. D. statement
of performance of characteristics and requirements. Answer: D Explanation: Policies are considered
the first and highest level of documentation, from which the lower level elements of standards,
procedures, and guidelines flow. This order , however, does not mean that policies are more
important than the lower elements. These higher–level policies, ... Show more content on
Helpwriting.net ...
So that external bodies will recognize the organizations commitment to security. D. So that they can
be held legally accountable. Answer: A Explanation: This really does not a reference as it should be
known. Upper management is legally accountable (up to 290 million fine). External organizations
answer is not really to pertinent (however it stated that other organizations will respect a BCP and
disaster recover plan). Employees need to be bound to the policy regardless of who signs it but it
gives validity. Ownership is the correct answer in this statement. However, here is a reference.
Fundamentally important to any security program 's success us the senior management 's high–
level statement of commitment to the information security policy process and a senior management
's understanding of how important security controls and protections are to the enterprise 's
continuity. Senior management must be Pass Any Exam. Any Time. – www..com 4
Ac
tua
lTe
sts
Explanation: Information security policies are high–level plans that describe the goals of the

procedures or controls. Policies describe security in general, not specifics. They provide the
blueprint fro an overall security program just as a specification defines your next product. – Roberta
Bragg CISSP Certification Training Guide (que) pg 587
.co
m
ISC CISSP: Practice Exam aware of the importance of security implementation to preserve the
organization 's viability (and for their own 'due care

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Facial Recognition System