Showing papers on "Three-dimensional face recognition 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. >

Face recognition based on depth and curvature features

Abstract: Face recognition from a representation based on features extracted from range images is explored. Depth and curvature features have several advantages over more traditional intensity-based features. Specifically, curvature descriptors have the potential for higher accuracy in describing surface-based events, are better suited to describe properties of the face in areas such as the cheeks, forehead, and chin, and are viewpoint invariant. Faces are represented in terms of a vector of feature descriptors. Comparisons between two faces is made based on their relationship in the feature space. The author provides a detailed analysis of the accuracy and discrimination of the particular features extracted, and the effectiveness of the recognition system for a test database of 24 faces. Recognition rates are in the range of 80% to 100%. In many cases, feature accuracy is limited more by surface resolution than by the extraction process. >

Human facial feature extraction for face interpretation and recognition

Abstract: This paper presents facial features extraction algorithms which can be used for automated visual interpretation and recognition of human faces. It is possible to capture the contours of eye and mouth by deformable template model because of their analytically describable shapes. However, the shapes of eyebrow, nostril and face are difficult to model using a deformable template. They are extracted by using an active contour model, the snake. >

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 face recognition method using higher order local autocorrelation and multivariate analysis

Abstract: Proposes a face recognition method which is characterized by structural simplicity, trainability and high speed. The method consists of two stages of feature extractions: first, higher order local autocorrelation features which are shift-invariant and additive are extracted from an input image; then those features are linearly combined on the basis of multivariate analysis methods so as to provide new effective features for face recognition in learning from examples. >

3D facial image analysis for human identification

Abstract: Presents a new method based on 3D facial section analysis for human face identification. The range data on different curves of intersection, such as, vertical, horizontal, and circular, with the 3D face are used as the distinguishing surface features. These are robust against changes in makeup and lighting as compared to the 2D approach, and more efficient for computation and data storage than other 3D approaches. The performance of this method for human face identification is evaluated through several matching experiments. It has been observed that the curves of intersection crossing the facial central area vertically and contain the features, like, nose and mouth, have the major distinctiveness. Those crossing near the inner corners of the eyes and a part of the nose are also effective for human face identification. But it seems that such an effectiveness requires the accurate extraction of the sections against their locational variations. >

Recognition of mixed facial expressions by neural network

Abstract: Deals with a neural network method for the machine recognition of mixed facial expressions by decomposing mixed facial expression into 2 or 3 components of 6 basic ones. The authors obtain the facial images, which show mixed facial expressions, from video tape recorded facial images and from the information of facial expressions in terms of the (x,y) coordinates of facial characteristic points. Then the position information of facial image is generated for 19 clients, and is used for the neural network training and recognition test. The recognition test is done by inputting the facial information, not being used in training the neural network, to the trained neural network. The recognition results obtained by the neural network are compared with those by humans. The neural network method is found to give a rather high agreement rate of about 70% compared with those obtained by humans. >

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. >

Extending the feature set for automatic face recognition

Abstract: Automatic face recognition has long been studied because it has a wide potential for application. Several systems have been developed to identify faces from small face populations via detailed face feature analysis, or by using neural nets, or through model based approaches. This study has aimed to provide satisfactory recognition within large populations of human faces and has concentrated on improving feature definition and extraction to establish an extended feature set to lead to a fully structured recognition system based on a single frontal view. An overall review on the development and the techniques of automatic face recognition is included, and performances of earlier systems are discussed. A novel profile description has been achieved from a frontal view of a face and is represented by a Walsh power spectrum which was selected from seven different descriptions due to its ability to distinguish the differences between profiles of different faces. A further feature has concerned the face contour which is extracted by iterative curve fitting and described by normalized Fourier descriptors. To accompany an extended set of geometric measurements, the eye region feature is described statistically by eye-centred moments. Hair texture has also been studied for the purpose of segmenting it from other parts of the face and to investigate the possibility of using it as a set of feature. These new features combine to form an extended feature vector to describe a face. The algorithms for feature extraction have been implemented on face images from different subjects and multiple views from the same person but without the face normal to the camera or without constant illumination. Features have been assessed in consequence on each feature set separately and on the composite feature vector. The results have continued to emphasize that though each description can be used to recognise a face there is a clear need for an extended feature set to cope with the requirements of recognizing faces within large populations.

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. >

Automatic face recognition

Abstract: A review of competing facial recognition techniques is presented. The authors then go on to introduce a new method of feature-based facial codeing allowing an entire face to be represented in less than two hundred bytes of information. Crucial to this coding process is the use of an a priori model of the face, which helps to guide the location and storage of the most important facial parts. The data reduction is thus achieved while still preserving many of the intrinsic facial recognition features. The algorithm used to perform the data reduction of the face is described. Results, for verification and recognition trials, are presented for a software implementation of the algorithm. >

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. >

Robust identification of human face using mosaic pattern and BPN

Abstract: The backpropagation network (BPN) is applied to human face recognition. A mosaic pattern transformed from the central part of a human face image is put into the BPN for personal identification. This combination succeeds in recognition of hundreds of people with robustness not only for defocused or noisy images but also for images of different face expressions or different ages. Hidden units of the BPN extract peculiar and delicate features of the face, which cannot be obtained from existing statistical methods. A few hidden units can especially select only men or women. Moreover, a BPN with an additional unit for processing unfamiliar faces is proposed. >

An architecture for face classification

Abstract: Biometric classification techniques, in which measurements of the physical characteristics of some distinguishing feature of the individual are made, have increasing application in the security field. Examples of biometric techniques include systems based upon signatures, fingerprinting, retinal scanning, hand geometry, and facial feature extraction. Of crucial importance in the application of such techniques is their acceptability to those who will be subjected to them, with obvious advantages for techniques that do not involve the physical constraint of the subject, and are otherwise unobtrusive. Automatic face identification offers the advantages discussed above. The authors discuss previous research into face identification, and present a face identification algorithm that would be suitable for implementation in a workstation security environment.

A novel automatic face recognition algorithm employing vector quantization

Abstract: Introduces a novel means of facial coding allowing an entire face to be represented in less than two hundred bytes of information. This data reduction is achieved while still preserving many of the intrinsic facial recognition features. The system could thus reduce an input face into a sufficiently small amount of information that it could be stored on a smart-card. The algorithm used to perform the data reduction of the face is described and the results, in verification and recognition trials, are presented for a software implementation of the algorithm. >

Temporal clues in handwriting

Abstract: Handwritten character recognition is typically classified as online or offline depending on the nature of the input data. Online data consists of a temporal sequence of instrument positions while offline data is in the form of a 2D image of the writing sample. Online recognition techniques have been relatively successful but have the disadvantage of requiring the data to be gathered during the writing process. This paper presents work on the extraction of temporal information from static images of handwriting and its implications for character recognition. >

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.

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. >

Massively parallel neural network recognition

Abstract: The author discusses two complete neural network recognition systems, a character recognition system and a fingerprint classification system. The requirements for a total vision system must include the capability for image isolation, segmentation, and feature extraction, as well as recognition. The systems were developed on a massively parallel array processor which was used to illustrate the importance of these higher-level functions. Both of these systems demonstrated state-of-the-art accuracy but both need improvements to be commercially viable. The issue in the character recognition system is to provide this accuracy at a speed compatible with commercial requirements of 1 page/s. This will require more sophisticated higher-level image parsing functions without loss of accuracy. The issue in fingerprint classification is the requirement for 99.7% accuracy at current speeds. >

Relative depth for behavior based recognition

Abstract: The authors propose to study object recognition by considering it in the context of an agent performing it in an environment, where the agent's intentions translate into a set of behaviors. The problem becomes a problem of action from intensity functions. In accomplishing a behavior, the next step of action from the images is determined. Acquiring the information for action is a solution for a recognition task. The recognition task is agent and behavior dependent and can use the output of different visual modules. The implementation of one visual module and its use for purposive recognition are described. It is shown how to robustly extract relative depth from a stereo setup without correspondence and calibration, and how this visual module can be used under some intentions and behaviors. For recognition under navigation behavior, relative depth was used to recognize obstacles by isolating unexpected objects in close range. For grasping behavior relative depth was used to recognize the different stages in the process. >

Color human-face image recognition based on statistical model

Abstract: A novel method of color human face image recognition is presented in this paper. First, an input colorface image is transformed into a monochrome image which contains enough useful information for recogni-flon. This monochrome image is then transformed into a standard image. The face recognition is completed

Target image extraction for face recognition using the sub-space classification method

Abstract: This paper proposes a scheme that offers robust txaaction of the target face image in standard view, which is defined using internal facial features as steady reference points. The scheme is characterized by two steps: facial feature detection using color image segmentation, and target image selection from among the candidates usil~g the subspace classification method. The scheme's flexibility has been confirmed in experiments under a wide range of image acquiat~on conditions.