Showing papers on "Face detection published in 1988"

The face-detection effect: configuration enhances detection.

Abstract: We have found that a picture of a face is more easily detected than is a pattern of arbitrarily rearranged facial features. An upright face is also more detectable than an inverted face. Using two-alternative forced-choice visual masking paradigms, we have found that this face-detection effect (FDE) can be produced with line drawings and with photocopies of a picture of a face. Our results suggest that a face, as an organized, meaningful pattern, is a more potent stimulus than an arbitrary assemblage of the same visual features. It may be that the FDE is a visual configuration effect. Previous visual configuration effects have been documented only with recognition responses. The FDE, by contrast, documents a configuration effect that affects the detectability of a stimulus.

Autonomous face recognition machine

Abstract: A machine is disclosed that is capable of locating human faces in video scenes with random content within two minutes, and capable of recognizing the faces that it locates. The machine uses images obtained from a video camera and is insensitive to variations in brightness, scale, focus, and operates without any human intervention or inputs. When a motion detection feature is included, (one of its options), the location and recognition events occur in less than 1 minute. One embodiment of the system uses: a camera, a Micro-Vax computer, an analog-go-digital A/D converter, and a hard copy print out to process video scenes with random content using an original computer program to locate human faces and identify them. In operation, the camera converts the video scenes into an analog electrical signal, which is converted into digital and forwarded to the computer. The computer performs an original pattern recognition algorithm to search for facial components, identify a gestalt face, and compare the gestalt-face's detected facial characteristics with a stored set of facial characteristics of known human faces, to identify the face thereby.

Partitioning polyhedral objects into nonintersecting parts

Abstract: An algorithm is described for partitioning intersecting polyhedrons into disjoint pieces and, more generally, removing intersections from sets of planar polygons embedded in three space. Polygons, or faces, need not be convex and may contain multiple holes. Intersections are removed by considering pairs of faces and slicing the faces apart along their regions of intersection. To reduce the number of face pairs examined, bounding boxes around groups of faces are checked for overlap. The intersection algorithm also computes set-theoretic operations on polyhedrons. Information gathered during face cutting is used to determine which portions of the original boundaries may be present in the result of an intersection, a union, or a difference of solids. The method includes provisions to detect and in some cases overcome, the effects of numerical inaccuracy on the topological decisions that the algorithm must make. The regions in which ambiguous results are possible are flagged so that the user can take appropriate action. >

Edge detection through residual analysis

Abstract: The authors provide theoretical justification for the use of zero crossings of residuals (between a filtered image and the original) for edge detection. The smoothed version is obtained by bilinear interpolation as a result of two-dimensional discrete regularization of subsampled images. The method is also applicable to smoothed images obtained by convolution with a Gaussian. Examples of applications of the method are shown for three kinds of pictures: aerial photographs, low-quality pictures of tools, and a high-quality picture of a face. The same parameters are used in all the examples. In addition they show examples of the results of one of the Canny edge detectors on the same pictures. >

Compensating for the reflectance of the human face

Abstract: A structured light imaging system is being developed which is capable of accurately recovering the three-dimensional surface of the human face. In structured light, a uniform square grid pattern is projected onto the face, and an image of this pattern is recorded using a single solid-state camera. By locating the intersections of the grid stripes and matching them correctly to the projected grid patterns, the three-dimensional positions of points on the face can be determined by triangulation. One of the major problems in locating the intersections of the grid pattern is the brightness variation due to the variations in the reflectivity of the human face. A solution to this problem is presented which is based on a simple statistical approximation of the reflectivity. A set of experiments is reported showing that the reflectance compensation improves the image processing used to detect and match the intersections. >