【正文】 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 prealigned 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 41 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 ie 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 eyesockets 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 templatebased method of eye localisation although providing fairly preciselocalisations often fails to locate the eyes pletely 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 pute the average distance from the eye template Figure 42 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 42 – Distance to the eye template for successful detections top indicating variance due to noise and failed detections bottom showing credible variance due to missdetected features In the lower image Figure 42 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 puted 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 43 When applied to the difference image before summing a total error this weighting scheme provides a much improved detection rate Figure 43 Eye template weights used to give higher priority to those pixels that best represent the eyes 42 The Direct Correlation Approach We begin our investigation into face recognition with perhaps the simplest approachknown as the direct correlation method also referred to as template matching by Brunelli and Poggio [ 29 ] involving the direct parison of pixel intensity values taken from facial images We use the term Direct Correlation to enpass all techniques in which face images are pared 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 Pearsons correlation is applied as the similarity function although such an approach would obviously e under our definition of direct correlation We typically use the Euclidean distance as our metric in these investigations inversely 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 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 65536dimensional 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 t