reports a face detection system based on clustering techniques. Their system, like ours, passes a small window over all portions of the image, and determines whether a face exists in each window. Their system uses a supervised clustering method with six ``face'' and six ``non-face'' clusters. Two distance metrics measure the distance of an input image to the prototype clusters. The first metric measures the ``partial'' distance between the test pattern and the cluster's 75 most significant eigenvectors. The second distance metric is the Euclidean distance between the test pattern and its projection in the 75 dimensional subspace. These distance measures have close ties with Principal Components Analysis (PCA), as described in . The last step in their system is to use either a perceptron or a neural network with a hidden layer, trained to classify points using the two distances to each of the clusters (a total of 24 inputs). Their system is trained with 4000 positive examples and nearly 47500 negative examples collected in the ``bootstrap'' manner. In comparison, our system uses approximately 16000 positive examples and 9000 negative examples.
Table 3 shows the accuracy of their system on Test Set B, along with the results of our system using the heuristics employed by Systems 10, 11, and 12 in Table 1. In , 149 faces were labelled in the test set, while we labelled 155. Some of these faces are difficult for either system to detect. Based on the assumption that  were unable to detect any of the six additional faces we labelled, the number of missed faces is six more than the values listed in their paper. It should be noted that because of implementation details,  process a slightly smaller number of windows over the entire test set; this is taken into account when computing the false detection rates. Table 3 shows that for equal numbers of false detections, we can achieve higher detection rates.
The main computational cost in  is in computing the two distance measures from each new window to 12 clusters. We estimate that this computation requires fifty times as many floating point operations as are needed to classify a window in our system, where the main costs are in preprocessing and applying neural networks to the window.
Table: Comparison of  and Our System on Test Set B
The candidate verification process used to speed up our system, described in Section 4, is similar to the detection technique used by . In that work, two networks were used. The first network has a single output, and like our system it is trained to produce a maximal positive value for centered faces, and a maximal negative value for non-faces. Unlike our system, for faces that are not perfectly centered, the network is trained to produce an intermediate value related to how far off-center the face is. This network scans over the image to produce candidate face locations. Unlike our candidate face detector, it must be applied at every pixel position. However, it runs quickly because of the network architecture: using retinal connections and shared weights, much of the computation required for one application of the detector can be reused at the adjacent pixel position. This optimization requires the preprocessing to have a restricted form, such that it takes as input the entire image, and produces as output a new image. The window-by-window preprocessing used in our system cannot be used. A second network is used for precise localization: it is trained to produce a positive response for an exactly centered face, and a negative response for faces which are not centered. It is not trained at all on non-faces. All candidates which produce a positive response from the second network are output as detections. One possible problem with this work is that the negative training examples are selected manually from a small set of images (indoor scenes, similar to those used for testing the system). It may be possible to make the detectors more robust using the bootstrap technique described here and in .
Another related system is described in . This system uses PCA to describe face patterns (as well as smaller patterns like eyes) with a lower-dimensional space than the image space. Rather than detecting faces, the main goal of this work is analyzing images of faces, to determine head orientation or to recognize individual people. However, it is also possible to use this lower-dimensional space for detection. A window of the input image can be projected into the face space and then projected back into the image space. The difference between the original and reconstructed images is a measure of how close the image is to being a face. Although the results reported are quite good, it is unlikely that this system is as robust as , because Pentland's classifier is a special case of Sung and Poggio's system, using a single positive cluster rather than six positive and six negative clusters.
 used an approach quite different from the ones presented above. Rather than having the computer learn the face patterns to be detected, the authors manually coded rules and feature detectors for face detection. Some parameters of the rules were then tuned based on a set of training images. Their algorithm proceeds in three phases. The first phase applies simple rules such as ``the eyes should be darker than the rest of the face'' to 4x4 pixel windows. All candidate faces are then passed to phase two, which applies similar (but more detailed) rules to higher resolution 8x8 pixel windows. Finally, all surviving candidates are passed to phase three, which used edge-based features to classify the full-resolution window as either a face or a non-face. The test set consisted of 60 digitized television images and photographs, each containing one face. Their system was able to detect 50 of these faces, with 28 false detections.