人脸识别文献

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

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.

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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 use

d. 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

inves

tigations (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

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

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d

threshold

?

reject

) .

Equ. 4-1

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

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

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