Showing papers on "Face detection published in 1994"

Face recognition under varying pose

Abstract: Researchers in computer vision and pattern recognition have worked on automatic techniques for recognizing human faces for the last 20 years. While some systems, especially template-based ones, have been quite successful on expressionless, frontal views of faces with controlled lighting, not much work has taken face recognizers beyond these narrow imaging conditions. Our goal is to build a face recognizer that works under varying pose, the difficult part of which is to handle face relations in depth. Building on successful template-based systems, our basic approach is to represent faces with templates from multiple model views that cover different poses from the viewing sphere. To recognize a novel view, the recognizer locates the eyes and nose features, uses these locations to geometrically register the input with model views, and then uses correlation on model templates to find the best match in the data base of people. Our system has achieved a recognition rate of 98% on a data base of 62 people containing 10 testing and 15 modeling views per person. >

Face detection using templates

Abstract: In the problem area of human facial image processing, the first computational task that needs to be solved is that of detecting a face under arbitrary scene conditions. Although some progress towards this has been reported in the literature, face detection remains a difficult problem. In this paper the authors report on a novel face-finding method that appears quite robust. First, "snakelets" are used to find candidate edges. Candidate ovals (face-locations) are then found from these snakelets using a voting method. For each of these candidate face-locations, the authors use a method introduced previously to find detailed facial features. If a substantial number of the facial features are found successfully, and their positions satisfy ratio-tests for being standard, the procedure positively reports the existence of a face at this location in the image.

Recognizing objects and faces

Abstract: We review evidence and theories concerning the processing mechanisms leading to the visual recognition of objects and faces. A good deal of work suggests that identification of objects at a basic level depends on edge-coding, whereas face recognition depends more on representations of surface properties such as colour and shading. Moreover, basic-level object recognition seems to involve a parts-based description, whereas face recognition depends upon more holistic processing. This work distinguishes between the visual processes mediating the recognition of objects and faces. However, when the demands of object recognition are made more similar to those of face recognition, then there appear to be some similarities in the perceptual representations used for objects and faces. Moreover, when we progress beyond the stage of perceptual representation to consider the organization of cognitive stages involved in the full identification of objects and faces, there are marked similarities in the process...

Face locating and tracking for human-computer interaction

Abstract: Effective human-to-human communication involves both auditory and visual modalities, providing robustness and naturalness in realistic communication situations. Recent efforts at our lab are aimed at providing such multimodal capabilities for human-machine communication. Most of the visual modalities require a stable image of a speaker's face. We propose a connectionist face tracker that manipulates camera orientation and room, to keep a person's face located at all times. The system operates in real time and can adapt rapidly to different lighting conditions, cameras and faces, making it robust against environmental variability. Extensions and integration of the system with a multimodal interface are presented. >

Tracking facial motion

Abstract: We describe a computer system that allows real-time tracking of facial expressions Sparse, fast visual measurements using 2D templates are used to observe the face of a subject Rather than track features on the face, the distributed response of a set of templates is used to characterize a given facial region These measurements ape coupled via a linear interpolation method to states in a physically-based model of facial animation, which includes both skin and muscle dynamics By integrating real-time 2D image-processing with 3D models we obtain a system that is able to quickly track and interpret complex facial motions >

Face recognition with eigenfaces

Abstract: Develops an approach to face recognition using eigenfaces, focusing on the effects of the eigenface used to represent a human face under several environment conditions. The authors first derive some computational feasible formula to find the eigenfaces, then investigate the relationship of mean absolute error between original face images and reconstructed images under various conditions such as face size, lighting and head orientation changes. The experimental results show that a large number of eigenfaces are not necessary to describe an individual face and only about 80 eigenfaces are sufficient for a large size set of face images. Gaussian smoothing can minimize the error under the same conditions. Finally, a face recognition system with eigenfaces and backpropagation neural network is implemented. >

Integration and Control of Reactive Visual Processes

Abstract: This paper describes a new approach to the integration and control of continuously operating visual processes. Visual processes are expressed as transformations which map signals from virtual sensors into commands for devices. These transformations define reactive processes which tightly couple perception and action. Such transformations may be used to control robotic devices, including fixation an active binocular head, as well as the to select and control the processes which interpret visual data.

Matching pursuit filters applied to face identification

Abstract: An algorithm has been developed for the automatic identification of human faces. Because the algorithm usesfacial features restricted to the nose and eye regions of the face, it is robust to variations in facial expression, hairstyle and the surrounding environment. The algorithm uses coarse to fine processing to estimate the location ofa small set of key facial features. Based on the hypothesized locations of the facial features, the identificationmodule searches the database for the identity of the unknown face. The identification is made by matching pursuitfilters. Matching pursuit filters have the advantage that they can be designed to find the differences between facialfeatures needed to identify unknown individuals. The algorithm is demonstrated on a database of 172 individuals. 1 Introduction There are many applications in modern society for a successful face identification system: nonintrusive identificationand verification for credit cards and ATM machines; nonintrusive access control to buildings and restricted areas;and monitoring of ports of entry for terrorists and smugglers. For the designer of pattern recognition algorithms,face recognition is a very challenging problem. The goal is to develop an algorithm that can differentiate among apopulation of three-dimensional curved objects that all have the same basic shape from databases whose size willvary from a couple of hundred individuals to over one million. The face itself is a dynamically varying object. Facialexpressions, make-up, facial hair and hair style all change from day to day. The conditions under which facial imageryis collected contribute to the difficulty of developing face recognition algorithms. The lighting, background, poseof the face, scale, and parameters of the acquisition are all variables in facial imagery collected under real-worldscenarios.A key to successfully developing a general face identification system is to systematically solve a sequence ofsubproblems of increasing complexity. One critical subproblem is the development of an algorithm that can identifyfaces from a gallery of full face frontal imagery. A gallery is the collection of images of known individuals; an imageof an unknown face presented to the algorithm is called a probe. The solution to this subproblem requires that thealgorithm implicitly handle the curved three-dimensional nature of the face and differentiate between the faces.

Model-assisted coding of video teleconferencing sequences at low bit rates

Abstract: We present a novel and practical way to integrate techniques from computer vision to low bit rate coding systems for video teleconferencing applications. Our focus is to locate and track the faces of persons in typical head-and-shoulders video sequences, and to exploit the face location information in a "classical" video coding/decoding system. The motivation is to enable the system to selectively encode various image areas and to produce psychologically pleasing coded images where faces are sharper. We refer to this approach as model-assisted coding. We propose a totally automatic, low-complexity algorithm, which robustly performs face detection and tracking. A priori assumptions regarding sequence content are minimal and the algorithm operates accurately even in cases of occlusion by moving objects. Face location information is exploited by a low bit rate 3D subband-based video coder which uses a model-assisted dynamic bit allocation with object-selective quantization. By transferring a small fraction of the total available bit rate from the non-facial to the facial area, the coder produces images with better-rendered facial features. The improvement was found to be perceptually significant on video sequences coded at 96 kbps for an input luminance signal in CIF format. The technique is applicable to any video coding scheme that allows for fine-grain quantizer selection (e.g. MPEG, H.261), and can maintain full decoder compatibility. >

A morphological model-driven approach to real-time road boundary detection for vision-based automotive systems

Abstract: This work presents a Computer Vision system for road boundary detection in automotive applications. Images are processed by a multiresolution algorithm, driven by a-priori knowledge through a top-down control. In order to face the hard real-time constraints of automotive tasks, a special purpose massively parallel computer architecture, PAPRICA, has been developed. The whole system is currently operative on MOB-LAB mobile laboratory: a land vehicle integrating the results of the activities of the Italian PROMETHEUS units. The basis of the algorithm is discussed using the formal tools of mathematical morphology, while the choice of the computing architecture and of the computational paradigm is explained. The generality of the presented approach allows its use also to solve similar problems, namely to detect features exploiting a long-distance correlation, such as the road boundaries in vehicular applications. >

Detection and pose estimation of human face with synthesized image models

Abstract: This paper presents a new method for detecting a human face, and estimating its pose while tracking it in real image sequences. The virtue of the method is that parameterized qualitative features derived from a lot of sampled facial images are introduced in the detection process, and in the face tracking process, some temporary model images of the face with various poses are synthesized by a texture mapping technique and utilized. While tracking the detected face, many model images are accumulated and the pose of the human face is estimated as a linear combination of correlations between the models.

Human face recognition using Dempster-Shafer theory

Abstract: This paper presents a novel approach to face recognition based on an application of the theory of evidence (Dempster-Shafer (1990) theory). Our technique makes use of a set of visual evidence derived from two projected views (frontal and profile) of the unknown person. The set of visual evidence and their associate hypotheses are subsequently combined using the Dempster's rule to output a ranked list of possible candidates. Image processing techniques developed for the extraction of the set of visual evidence, the formulation of the face recognition problem within the framework of Dempster-Shafer theory and the design of suitable mass functions for belief assignment are discussed. The feasibility of the technique was demonstrated in an experiment. >

An anthropological model for automatic recognition of the male human face

Abstract: SummaryThe human face is a characteristic pattern most familiar to us when distinguishing people. Although recognizing human faces is one of our everyday activities, we are mostly not aware how the mechanisms of recognition actually work. Attempts to recognize the human face by machine are rarer (less frequent) than those of the recognition of some other phenomena in everyday life. This paper describes the automated analysis of a human face from the grey level picture, defined as an individual description of a face, given founded on the anthropological model of a human face that incorporates 19 facial parameters of a male face in norma facialis. On the basis of these parameters it is possible to analyse, recognize and identify the human face. The contour image is used as an input to the pattern analysis program. Some algorithms for the search of characteristic face areas are presented. The Hough transform for an ellipse is used to determine the position of the head in a grey image, and to define the eye r...

Extraction of facial images from a complex background using SGLD matrices

Abstract: This paper proposed a new method for the face extraction from a complex background. Based on the space gray level dependence (SGLD) matrices, the facial texture model composed by a set of inequalities was derived. Using this textural model, the authors designed a kind of scanning scheme for face detection in the complex backgrounds. An experiment using 60 images containing 150 faces gave the error rate 0% with the false alarm rate 5.4%.

Machine vision recognition of facial affect using backpropagation neural networks

Abstract: A machine vision system has been developed to classify posed facial expressions indicative of eight discrete emotions: interest, happiness, sadness, surprise, anger, fear, contempt, and disgust. The system consists of an image capture subsystem to capture and store color images of facial affect; a face detection subsystem to distinguish image corresponding to a face from image corresponding to scene background; an edge detection subsystem to locate image regions corresponding to edges; a face feature detection subsystem to locate edge clusters corresponding to eyes, eye brows, and lips; and a face feature analysis subsystem consisting of eight backpropagation neural networks to classify edge dusters corresponding to facial features into the aforementioned emotion categories. An image database was created for system development and verification and consisted of facial affect captured from nine racially diverse males and females. System accuracy ranged from 68% to 89% across emotions and across the set of image resolutions utilized.

Human face recognition using neural networks

Abstract: The paper describes the development of a human face recognition system (HFRS) using multilayer perceptron artificial neural networks (MLP). The MLP network is trained with a set of face images until it is in a "learned" state. The network is capable of classifying the face input into its class. In the case of the subject face is not one of those trained, the network will register it as unknown. The system, which takes the face image input from video camera, is also developed to detect the presence of an object in front of the camera and to search for the human facial area automatically. The detected facial area is then used as the inputs to the neural network to perform recognition. >

Human face detecting device

Abstract: PURPOSE:To provide a human face detecting device which is strong to the size change and the parallel/turning movements of faces and also to the fluctuation in the illuminating conditions. CONSTITUTION:The human face detecting device consists of an area detecting device 21 which inputs an image including a human face and segments the face component elements as areas, a face candidate detecting device 22 which outputs the face candidates based on the sizes and the positional relations among those face component element areas, and a face deciding device 23 which finely checks the face candidates to decide that it is a face or not.

Segmentation and estimation of image motion by a robust method

Abstract: Locating the image of moving objects in a scene and estimating their motion are among the main issues in dynamic scene analysis. They are relevant to a large variety of tasks such as autonomous navigation, tracking, obstacle detection and surveillance. A central problem faced by existing methods is related to the occurrence of motion boundaries. With the aim of achieving correct and robust interpretation in the face of occurrence of motion boundaries, noise, and insufficiently informative image brightness pattern regions, the method we propose is articulated about the fundamental concepts of gradient-based multiconstraint, statistically robust regression, and regularization. >

A local feature based human face recognition system

Abstract: A local recognition system is introduced for the recognition of human faces. The system uses a novel focal rotation invariant feature classifier to enable the rapid robust classification of "interesting" local regions. This information is then used to identify human faces under various conditions. >

Model-based versus knowledge-guided representation of non-rigid objects: a case study

Abstract: Two different approaches to the detection and representation of the mouth in real video images of the human face are investigated. The model-based approach presented is based on a technique known as "deformable templates", and tries to approximate the contours of the lips with a model consisting of four parabolas. An alternative to the model-based approach, referred to as the knowledge-guided approach, is proposed. The basic idea is not to try to capture all of the a priori knowledge about an object in a single global model that is adapted to the image, but rather to utilize the a priori knowledge in a step-by-step way, in order to refine rough initial hypotheses into a compact description of the object. This method may be interpreted as a gradual concentration on the relevant structures in the image. The combination of the resulting structures yields a compact description of the object. In the application, which is the basis for this investigation, the goal is to enhance speech recognition by using visual information about lip movements in addition to the acoustic signal. Only the problem of finding an accurate and robust representation of the lips in an image is addressed. Each of the methods were investigated for the same set of 15 faces. Our experiments indicate that the knowledge-guided approach performs more accurately and more robustly, than the model-based approach. >

Personal identification by image-processing sequential images of human-face

Abstract: Personal identification by image-processing is an important technique for personal reference or verification which can serve a highly valuable service in an information based society. But, it is difficult to identify a person with image processing by computer because a human face is changeable. Although it is difficult, the personal identification from facial images is an attractive subject in computer systems because of its wide application. Therefore, the auhtors propose a method of personal identification from a series of photographic images taken naturally. The method is pattern matching of salient features, constructed automatically from facial images. In this study, in order to approach the realization of a human interface, the main stress falls on automatic salient feature extraction using sequential photographic images taken in normal conditions. At first, from sequential images, eyes coordinates are extracted by detecting eyes winking. Based on these coordinates a full description of the face is defined. Finally, a mosaic is applied to that face to be used for identification. Experimental results show that the proposed method is robust and valid for numerous kinds of facial images in real scenes. >

Pose estimation of human face using synthesized model images

Abstract: This paper presents a new method for pose estimation of the human face. The virtue of the method is the use a very simple calculation; correlation among multiple model images. It does not use any facial features such as organs or face gestalts. It is an improvement of the conventional template matching method, but to cope with deformation of facial pattern caused by its rotation or translation, some temporary model images are synthesized by using the generic S-dimensional shape model of the human face. A region of a face with various view directions is detected and then accumulated as a model image. It is arranged in "pose space", according to its estimated view direction, and the pose of the face is estimated by a simple calculation of the correlation between multiple model images accumulated by that time. An experimental result using real moving images of various human faces is shown. >

Human face profile recognition

Abstract: The purpose of this thesis is to implement an automatic person identification system based on face profiles. Each person's face profile can be quite unique within a small sample population and therefore it can be used as the basis of an automatic person identification system. To quantify human face profiles for use in the recognition system, Fourier descriptors are used to describe the open curve extracted from a face profile. Fourier descriptors in the low-frequency range are shown to be useful for human face profile recognition. By using 16 Fourier coefficients, a correct recognition rate of 92% for 60 subjects was achieved.

A method for identifying individuals using angled facial data obtained by bright-spots matrix projection

Abstract: We have already proposed a method for identifying human faces using the three dimensional facial data obtained by setting the fiber grating vision sensor in front of the faces. But in the previous method, there are some problems that these facial data include redundant information because of the bilateral symmetry of the human faces, and in some case, the data behind the nose can't be obtained. In this paper, the authors describe a method for identifying individuals using the angled facial data obtained from the fiber grating vision sensor. We think that we can obtain the more effective data for identifying the faces by setting the fiber grating vision sensor at an angle to the faces. Because we can obtain the effective information from the large region of one side of the faces when we set the sensor like this. In this method, the fiber grating vision sensor which has been developed by the authors, is employed for the three dimensional shape of the faces. Before identifying the facial data, it is necessary to calibrate the position and direction of the facial data. In this method, a set of the directions of normal vectors at data points on the facial surface is obtained, and calibrations are carried out in accordance with the extend of errors in the sets. To identify the human faces, a multi-layered neural network is used in which the inputs are two component values of normal vector on the facial surface. The experiments using the experimental system are performed to demonstrate the efficacy of this method and the experimental results are shown. >

Face identification using algebraic features of images

Abstract: This paper proposes a new technique for the identification of face images. The basis idea is that the front face images of a person are considered as the samples coming from multiple classes, each class corresponding to the face images of one head orientation. Therefore, for each person, we can take his front facial images from a number of head orientations as training data based on which an algebraic feature extractor and a classifier can be built for this person. The problems of feature extraction, classifier design, face verification and recognition are discussed in this paper. Experimental results are also provided.