Showing papers on "Face detection published in 1992"

A feature based approach to face recognition

Abstract: A feature-based approach to face recognition in which the features are derived from the intensity data without assuming any knowledge of the face structure is presented. The feature extraction model is biologically motivated, and the locations of the features often correspond to salient facial features such as the eyes, nose, etc. Topological graphs are used to represent relations between features, and a simple deterministic graph-matching scheme that exploits the basic structure is used to recognize familiar faces from a database. Each of the stages in the system can be fully implemented in parallel to achieve real-time recognition. Experimental results for a 128*128 image with very little noise are evaluated. >

Robust detection of facial features by generalized symmetry

Abstract: Locating facial features is crucial for various face recognition schemes. The authors suggest a robust facial feature detector based on a generalized symmetry interest operator. No special tuning is required if the face occupies 15-60% of the image. The operator was tested on a large face data base with a success rate of over 95%. >

A robust algebraic method for human face recognition

Abstract: The feature image and projective image are first proposed to describe the human face, and a new method for human face recognition in which projective images are used for classification is presented. The projective coordinates of projective image on feature images are used as the feature vectors which represent the inherent attributes of human faces. Finally, the feature extraction method of human face images is derived and a hierarchical distance classifier for human face recognition is constructed. The experiments have shown that the recognition method based on the coordinate feature vector is a powerful method for recognizing human face images, and recognition accuracies of 100 percent are obtained for all 64 facial images in eight classes of human faces. >

What the human face tells the human mind: some challenges for the robot-human interface

Abstract: The human face serves a variety of different communicative functions in social interaction. The face mediates person identification, the perception of emotional expressions and lipreading. Perceiving the direction of social attention, and facial attractiveness, also affect interpersonal behaviour. This paper reviews these different uses made of facial information, and considers their computational demands. The possible link between the perception of faces and deeper levels of social understanding is emphasised through a discussion of developmental deficits affecting social cognition. Finally, the implications for the development of communication between robots and humans is discussed. It is concluded that it could be useful both for robots to understand human faces, and also to display human-like facial gestures themselves. >

An accurate and robust face identification scheme

Abstract: Proposes a scheme that offers accurate and robust identification of human faces. The scheme is characterized by four aspects: facial feature detection using color image segmentation; target image extraction using a sub-space classification method; robust feature extraction based on K-L expansion of an invariant feature space; and face classifier training based on 3D CG modeling of the human face. The scheme's flexibility under a wide range of image acquisition conditions has been confirmed through the assessment of an experimental face identification system. >

A robust method for detecting image features with application to face recognition and motion correspondence

Abstract: The authors present an approach to feature detection, which is a fundamental issue in many intermediate-level vision problems such as stereo, motion correspondence, image registration, etc. The approach is based on a scale-interaction model of the end-inhibition property exhibited by certain cells in the visual- cortex of mammals. These feature detector cells are responsive to short lines, line endings, corners and other such sharp changes in curvature. In addition, this method also provides a compact representation of feature information which is useful in shape recognition problems. Application to face recognition and motion correspondence are illustrated. >

Recognising face features and faces

Abstract: In order to develop a successful face-recognition system, it is necessary to remove instance-specific detail from an incoming image, before attempting to match against previously stored codes. Very recently hybrid methods have emerged, which make use of known feature locations either implicitly, or explicitly to provide much better input to a recognition component which has many of the characteristics of a net based method. The author describes one such hybrid scheme, presenting details of a working system for locating face features; and a coding scheme, based on accurate feature location, which is useful for recognition. The author describes some applications. An advantage of feature recognition over net-based methods is the detailed understanding available at an intermediate stage; this can sometimes be valuable in its own right.

Caption-aided face location in newspaper photographs

Abstract: The human face is an object that is easily located in complex scenes by humans and adults alike. Yet the development of an automated system to perform this task is extremely challenging. This paper is about developing computational procedures to locate human faces in newspaper photographs where scenes are often cluttered making object location non-trivial. On the other hand, the task is made feasible by constraints which follow naturally from rules in photo-journalism. Faces identified by the caption are clearly depicted without occlusion and contrast against the background and sizes of faces fall within a fixed range depending on the dimensions of the photograph and the number of people featuring in it. >

Multiresolution scene segmentation using MLPs

Abstract: The authors discuss the application of neural networks on scene segmentation problems. They demonstrate that a multiresolution image analysis followed by a scanning of smoothed images using a time delay neural network can provide solutions. This approach was applied on a task of human facial detection and localization in scenes. The system allowed the detection of 90% of the faces present in the scene. Current work on the integration of this segmentation module into a global system is discussed. >

Recognition of human face using fuzzy inference and neural network

Abstract: This paper proposes an image extraction technique of human face by color image processing, fuzzy inference, and a neural network. The image processing technique provides a method of extraction of the skin color and measurement of the detailed information of a human face. The fuzzy inference estimates the facial direction based on the configuration of the extracted face candidates, while the neural network distinguishes faces from the others features using the detailed information and the facial, direction. This method makes it possible to recognize not only a full face but also side faces. Further, the authors show how this method works for the illusion problem. The extraction accuracy of this present system is 80 percent. According to the experimental results, this system can be used for the vision system of autonomous robots under a robot-human mixed environment. >

Invariant object recognition via surface reconstruction neural networks

Abstract: Classifying objects that are distorted by similarity transform and detection/occlusion noise is a difficult pattern recognition task. The authors present a novel and robust neural network solution based on detected surface boundary points. The method operates in two stages. The object is first parametrically represented by a surface reconstruction neural network (SRNN) trained by the boundary points sampled from the exemplar object. When later presented with distorted object without point correspondence, this parametric representation reduces the effects caused by detection/occlusion and also allows the mismatch information back-propagated through the SRNN to iteratively determine the best similarity transform of the distorted object. The distance measure can then be computed in the reconstructed representation domains between the exemplar object and the aligned distorted object. >

A computational theory for locating human faces in photographs

Abstract: The human face is an object that is easily located in complex scenes by infants and adults alike. Yet the development of an automated system to perform this task is extremely challenging. An attempt to solve this problem raises two important issues in object location. First, natural objects such as human faces tend to have boundaries which as yet have not been accurately described by analytical functions. This renders the commonly used parameter-based techniques like the Hough transform inadequate for extracting the shape. Second, the object of interest could occur in a scene in various sizes, thus requiring scale independent techniques which can detect instances of the object at all scales. Although, the task of identifying a well-framed face (as one of a set of labeled faces) has been well researched, the task of locating a face in a natural scene is relatively unexplored. We present a computational theory for locating human faces in scenes with certain constraints. Our experiments will be confined to instances where people's faces are the primary subject of the scene, occlusion is minimal, and the faces contrast well against the background. A hypothesis generate-and-test paradigm is proposed and justified as a methodology for face location. Alternative methods of hypothesis testing by either performing rigorous face-specific analysis or using collateral information have been addressed. The shape of the object is defined in terms of features selected using cognitive principles of human perception. Geometrical relationships between features are not rigid. Rather, they are represented by spring functionals that allow several configurations of the features to match against the model. The framework of spring functionals provides a mathematical basis for evaluating the "goodness" of matches between the data and the model.

Making faces: the facial animation, construction and editing system

Abstract: The human face is a fascinating, but extremely complex object; the research project described is concerned with the computer generation and animation of faces. However, the age old captivation with the face transforms into a major obstacle when creating synthetic faces. The face and head are the most visible attributes of a person. We master the skills of recognising faces and interpreting facial movement at a very early age. As a result, we are likely to notice the smallest deviation from our concept of how a face should appear and behave. Computer animation in general, is often perceived to be ``wooden'' and very ``rigid''; the aim is therefore to provide facilities for the generation of believable faces and convincing facial movement. The major issues addressed within the project concern the modelling of a large variety of faces and their animation. Computer modelling of arbitrary faces is an area that has received relatively little attention in comparison with the animation of faces. Another problem that has been considered is that of providing the user with adequate and effective control over the modelling and animation of the face. The Facial Animation, Construction and Editing System or FACES was conceived as a system for investigating these issues. A promising approach is to look a little deeper than the surface of the skin. A three-layer anatomical model of the head, which incorporates bone, muscle, skin and surface features, has been developed. As well as serving as a foundation which integrates all the facilities available within FACES, the advantage of the model is that it allows differing strategies to be used for modelling and animation. FACES is an interactive system, which helps with both the generation and animation of faces, while hiding the structural complexities of the face from the user. The software consists of four sub-systems; CONSTRUCT and MODIFY cater for modelling functionality, while ANIMATE allows animation sequences to be generated and RENDER provides for shading and motion evaluation.

Gate type car washing machine

Abstract: PURPOSE:To shorten the time required for washing a car by finding a proper position of end face blushing according to the position of the end face of a car which is detected during a preliminary process, and blushing the car in this proper position during a subsequent process, and controlling operation of a car washing machine so that the machine washes the car without retreating. CONSTITUTION:A control board 20 is provided with a travel position detection means (a) for counting pulse signals which are generated by a rotary encoder 16 as a main body 1 travels, a first end face detection means (b) for identifying the input of any detection signal from switches 12, 12 as detection of the end face of a car when the main body 1 travels with side blushes 3, 3 being closed, and a second end face detection means (c) for detecting the closed state of each side blush 3, 3 at switches 13, 13 after the side face of the car body is washed. A predetermined travel position of the main body decided when the end face is detected by each end face detection means (b), (c) is sampled by a proper position setting means (d) from the detection means (a) and a proper position is given according to this travel position and car washing operation is controlled by a control means (e) according to each end face detection signal and the proper position.