ethanol人脸识别论文文献翻译中英文

2021年1月21日发(作者：阿拉伯语我爱你)
人脸识别论文文献翻译中英文

人脸识别论文中英文

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原文及译文
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翻译原文来自

Thomas David Heseltine BSc. Hons. The University of York
Department of Computer Science
For the Qualification of PhD. -- September 2005 -
《
Face Recognition: Two- Dimensional and Three-Dimensional
Techniques
》

4 Two-dimensional Face Recognition
4.1 Feature Localization
Before discussing the methods of comparing two facial images we now
take a brief look at some at the preliminary processes of facial feature
alignment. This process typically consists of two stages: face detection
and eye localisation. Depending on the application, if the position of
the face within the image is known beforehand (for a cooperative subject
in a door access system for example) then the face detection stage can
often be skipped, as the region of interest is already known. Therefore,
we discuss eye localisation here, with a brief discussion of face
detection in the literature review(section 3.1.1).
The eye localisation method is used to align the 2D face images of
the various test sets used throughout this section. However, to ensure
that all results presented are
representative of the face recognition accuracy and not a product of
the performance of the eye localisation routine, all image alignments
are manually checked and any errors corrected, prior to testing and
evaluation.
We detect the position of the eyes within an image using a simple
template based method. A training set of manually pre- aligned images of
faces is taken, and each image cropped to an area around both eyes. The
average image is calculated and used as a template.

Figure 4-1 - The average eyes. Used as a template for eye detection.
Both eyes are included in a single template, rather than
individually searching for each eye in turn, as the characteristic
symmetry of the eyes either side of the nose, provides a useful feature
that helps distinguish between the eyes and other false positives that
may be picked up in the background. Although this method is highly
susceptible to scale(i.e. subject distance from the
camera) and also introduces the assumption that eyes in the image
appear near horizontal. Some preliminary experimentation also reveals
that it is advantageous to include the area of skin just beneath the
eyes. The reason being that in some cases the eyebrows can closely match
the template, particularly if there are shadows in the eye- sockets, but
the area of skin below the eyes helps to distinguish the eyes from
eyebrows (the area just below the eyebrows contain eyes, whereas the
area below the eyes contains only plain skin).
A window is passed over the test images and the absolute difference
taken to that of the average eye image shown above. The area of the
image with the lowest difference is taken as the region of interest
containing the eyes. Applying the same procedure using a smaller
template of the individual left and right eyes then refines each eye
position.
This basic template-based method of eye localisation, although
providing fairly preciselocalisations, often fails to locate the eyes
completely. However, we are able to improve performance by including a
weighting scheme.
Eye localisation is performed on the set of training images, which
is then separated into two sets: those in which eye detection was
successful; and those in which eye detection failed. Taking the set of
successful localisations we compute the average distance from the eye
template (Figure 4-2 top). Note that the image is quite dark, indicating
that the detected eyes correlate closely to the eye template, as we
would expect. However, bright points do occur near the whites of the eye,
suggesting that this area is often inconsistent, varying greatly from
the average eye template.

Figure 4-2
–
Distance to the eye template for successful detections
(top) indicating variance due to
noise and failed detections (bottom) showing credible variance due
to miss-detected features.
In the lower image (Figure 4-2 bottom), we have taken the set of
failed localisations(images of the forehead, nose, cheeks, background
etc. falsely detected by the localisation routine) and once again
computed the average distance from the eye template. The bright pupils
surrounded by darker areas indicate that a failed match is often due to
the high correlation of the nose and cheekbone regions overwhelming the
poorly correlated pupils. Wanting to emphasise the
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difference of the pupil regions for these failed matches and
minimise the variance of the whites of the eyes for successful matches,
we divide the lower image values by the upper image to produce a weights
vector as shown in Figure 4-3. When applied to the difference image
before summing a total error, this weighting scheme provides a much
improved detection rate.

Figure 4-3 - Eye template weights used to give higher priority to
those pixels that best represent the eyes.
4.2 The Direct Correlation Approach
We begin our investigation into face recognition with perhaps the
simplest approach,known as the direct correlation method (also referred
to as template matching by Brunelli and Poggio [ 29 ]) involving the
direct comparison of pixel intensity values taken from facial images. We
use the term ‘Direct Correlation’ to encompass all techniques in which
face images are compared directly, without any form of image space
analysis, weighting schemes or feature extraction, regardless of the
distance metric used. Therefore, we do not infer that Pearson’s
correlation is applied as the similarity function (although such an
approach would obviously come under our definition of direct
correlation). We typically use the Euclidean distance as our metric in
these investigations (inversely related to Pearson’s correlation and
can be considered as a scale and translation sensitive form of image
correlation), as this persists with the contrast made between image
space and subspace approaches in later sections.
Firstly, all facial images must be aligned such that the eye centres
are located at two specified pixel coordinates and the image cropped to
remove any background
information. These images are stored as greyscale bitmaps of 65 by
82 pixels and prior to recognition converted into a vector of 5330
elements (each element containing the corresponding pixel intensity
value). Each corresponding vector can be thought of as describing a
point within a 5330 dimensional image space. This simple principle can
easily be extended to much larger images: a 256 by 256 pixel image
occupies a single point in 65,536-dimensional image space and again,
similar images occupy close points within that space. Likewise, similar
faces are located close together within the image space, while
dissimilar faces are spaced far apart. Calculating the Euclidean
distance d, between two facial image vectors (often referred to as the
query image q, and gallery image g), we get an indication of
similarity. A threshold is then
applied to make the final verification decision.

d q g (d threshold ?accept d threshold ?reject ) . Equ. 4-1
3
4.2.1 Verification Tests
The primary concern in any face recognition system is its ability to
correctly verify a claimed identity or determine a person's most likely
identity from a set of potential matches in a database. In order to
assess a given system’s ability to perform these tasks, a variety of
evaluation methodologies have arisen. Some of these analysis methods
simulate a specific mode of operation (i.e. secure site access or
surveillance), while others provide a more mathematical description of
data distribution in some
classification space. In addition, the results generated from each
analysis method may be presented in a variety of formats. Throughout the
experimentations in this thesis, we primarily use the verification test
as our method of analysis and comparison, although we also use Fisher’s
Linear Discriminant to analyse individual subspace components in section
7 and the identification test for the final evaluations described in
section 8. The verification test measures a system’s ability to
correctly accept or reject the proposed identity of an individual. At a
functional level, this reduces to two images being presented for
comparison, for which the system must return either an acceptance (the
two images are of the same person) or rejection (the two images are of
different people). The test is designed to simulate the application area
of secure site access. In this scenario, a subject will present some
form of identification at a point of entry, perhaps as a swipe card,
proximity chip or PIN number. This number is then used to retrieve a
stored image from a database of known subjects (often referred to as the
target or gallery image) and compared with a live image captured at the
point of entry (the query image). Access is then granted depending on
the acceptance/rejection decision.
The results of the test are calculated according to how many times
the accept/reject decision is made correctly. In order to execute this
test we must first define our test set of face images. Although the
number of images in the test set does not affect the results produced
(as the error rates are specified as percentages of image comparisons),
it is important to ensure that the test set is sufficiently large such
that statistical anomalies become insignificant (for example, a couple
of badly aligned images matching well). Also, the type of images (high
variation in lighting, partial occlusions etc.) will significantly alter
the results of the test. Therefore, in order to compare multiple face
recognition systems, they must be applied to the same test set.
However, it should also be noted that if the results are to be
representative of system performance in a real world situation, then the
test data should be captured under precisely the same circumstances as
in the application the other hand, if the purpose of the
experimentation is to evaluate and improve a method of face recognition,
which may be applied to a range of application environments, then the
test data should present the range of difficulties that are to be
overcome. This may mean including a greater percentage of ‘difficult’
images than
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would be expected in the perceived operating conditions and hence
higher error rates in the results produced. Below we provide the
algorithm for executing the verification test. The algorithm is applied
to a single test set of face images, using a single function call to the
face recognition algorithm: CompareFaces(FaceA, FaceB). This call is
used to compare two facial images, returning a distance score indicating
how dissimilar the two face images are: the lower the score the more
similar the two face images. Ideally, images of the same face should
produce low scores, while images of different faces should produce high
scores.
Every image is compared with every other image, no image is compared
with itself and no pair is compared more than once (we assume that the
relationship is symmetrical). Once two images have been compared,
producing a similarity score, the ground-truth is used to determine if
the images are of the same person or different people. In practical
tests this information is often encapsulated as part of the image
filename (by means of a unique person identifier). Scores are then
stored in one of two lists: a list containing scores produced by
comparing images of different people and a list containing scores
produced by comparing images of the same person. The final
acceptance/rejection decision is made by application of a threshold. Any
incorrect decision is recorded as either a false acceptance or false
rejection. The false rejection rate (FRR) is calculated as the
percentage of scores from the same people that were classified as
rejections. The false acceptance rate (FAR) is calculated as the
percentage of scores from different people that were classified as
acceptances.
For IndexA = 0 to length(TestSet)
For IndexB = IndexA+1 to length(TestSet)
Score = CompareFaces(TestSet[IndexA], TestSet[IndexB])
If IndexA and IndexB are the same person
Append Score to AcceptScoresList
Else
Append Score to RejectScoresList
For Threshold = Minimum Score to Maximum Score:
FalseAcceptCount, FalseRejectCount = 0
For each Score in RejectScoresList
If Score <= Threshold
Increase FalseAcceptCount
For each Score in AcceptScoresList
If Score > Threshold
Increase FalseRejectCount
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FalseAcceptRate = FalseAcceptCount / Length(AcceptScoresList)
FalseRejectRate = FalseRejectCount / length(RejectScoresList)
Add plot to error curve at (FalseRejectRate, FalseAcceptRate)
These two error rates express the inadequacies of the system when
operating at a specific threshold value. Ideally, both these figures
should be zero, but in reality reducing either the FAR or FRR (by
altering the threshold value) will inevitably result in increasing the
other. Therefore, in order to describe the full operating range of a
particular system, we vary the threshold value through the entire range
of scores produced. The application of each threshold value produces an
additional FAR, FRR pair, which when plotted on a graph produces the
error rate curve shown below.

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Figure 4-5 - Example Error Rate Curve produced by the verification
test.
The equal error rate (EER) can be seen as the point at which FAR is
equal to FRR. This EER value is often used as a single figure
representing the general recognition performance of a biometric system
and allows for easy visual comparison of multiple methods. However, it
is important to note that the EER does not indicate the level of error
that would be expected in a real world application. It is unlikely that
any real system would use a threshold value such that the percentage of
false acceptances were equal to the percentage of false rejections.
Secure site access systems would typically set the threshold such that
false acceptances were significantly lower than false rejections:
unwilling to tolerate intruders at the cost of inconvenient access
denials. Surveillance systems on the other hand would require low false
rejection rates to successfully identify people in a less controlled
environment. Therefore we should bear in mind that a system with a lower
EER might not necessarily be the better performer towards the extremes
of its operating capability.
There is a strong connection between the above graph and the
receiver operating characteristic (ROC) curves, also used in such
experiments. Both graphs are simply two visualisations of the same
results, in that the ROC format uses the True Acceptance Rate(TAR),
where TAR = 1.0
–
FRR in place of the FRR, effectively flipping the