1、人脸识别文献翻译中英双文4 Two-dimensional Face Recognition4.1 Feature LocalizationBefore 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 localiza
2、tion. 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 localization
3、here, with a brief discussion of face detection in the literature review .The eye localization 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
4、 product of the performance of the eye localization 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 f
5、aces 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 cha
6、racteristic symmetry of the eyes either side of the nose, provide 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 introduce
7、s 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
8、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 avera
9、ge 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 localiz
10、ation, although providing fairly precise localizations, often fails to locate the eyes completely. However, we are able to improve performance by including a weighting scheme.Eye localization is performed on the set of training images, which is then separated into two sets: those in which eye detect
11、ion was successful; and those in which eye detection failed. Taking the set of successful localizations 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 expe
12、ct. 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) show
13、ing credible variance due to miss-detected features.In the lower image (Figure 4-2 bottom), we have taken the set of failed localizations(images of the forehead, nose, cheeks, background etc. falsely detected by the localization routine) and once again computed the average distance from the eye temp
14、late. 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 emphasize the difference of the pupil regions for these failed matches and minimize the varianc
15、e 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 - Ey
16、e template weights used to give higher priority to those pixels that best represent the eyes.4.2 The Direct Correlation ApproachWe begin our investigation into face recognition with perhaps the simplest approach, known as the direct correlation method (also referred to as template matching by Brunel
17、li and Poggio) 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
18、of the distance metric used. Therefore, we do not infer that Pearsons 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 (inversel
19、y related to Pearsons 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 centers are loca
20、ted at two specified pixel coordinates and the image cropped to remove any background information. These images are stored as grayscale 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
21、 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 wi
22、thin 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 simil
23、arity. A threshold is then applied to make the final verification decision.4.2.1 Verification TestsThe primary concern in any face recognition system is its ability to correctly verify a claimed identity or determine a persons most likely identity from a set of potential matches in a database. In or
24、der to assess a given systems 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 i
25、n 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 Fishers Linear Dis
26、criminate to analyze individual subspace components in section 7 and the identification test for the final evaluations described in section 8. The verification test measures a systems ability to correctly accept or reject the proposed identity of an individual. At a functional level, this reduces to
27、 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 wi
28、ll 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 p
29、oint 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. Alt
30、hough 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 bad
31、ly 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 no
32、ted 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 environment. On the other hand, if the purpose of the experimentation is to evaluate and improve a meth
33、od 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
