Showing papers on "Facial recognition system 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. >

Face processing in psychiatric conditions.

Abstract: Functional models of face processing have indicated that dissociations exist between the various processes involved, e.g. between familiar face recognition and matching of unfamiliar faces, and between familiar face recognition and facial expression analysis. These models have been successfully applied to the understanding of the different types of impairment that can exist in neuropsychological patients. In the present study, aspects of face processing in psychiatric patients were investigated in relation to Bruce & Young's (1986) model. Based on this functional model different predictions can be made. We contrast here the impaired expression analysis hypothesis, which is that psychiatric patients would show a deficit in facial expression recognition, but not in facial identity recognition or unfamiliar face matching, with the generalized deficit hypothesis, that patients would be impaired on all tasks. These hypotheses were examined using three forced-choice tasks (facial recognition, facial expression recognition, and unfamiliar face matching) which were presented to schizophrenic and depressed patients, and to non-patient controls. Results showed that schizophrenic patients performed at a significantly lower level than non-patient controls on all three tasks, supporting the generalized deficit hypothesis.

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%. >

Face Recognition through Geometrical Features

Abstract: Several different techniques have been proposed for computer recognition of human faces. This paper presents the first results of an ongoing project to compare several recognition strategies on a common database.

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. >

Recognition of Six basic facial expression and their strength by neural network

Abstract: Develops an 'Active human interface' that realizes interactive communication between machine (computer and/or robot) and human. The authors investigate the method of machine recognition of human facial expressions and their strength. They deal with the neural network method of recognition of facial expressions. Considering 6 groups of facial expressions, i.e. surprise, fear, disgust, anger, happiness and sadness, they obtain 30 x- and y-coordinates of facial characteristic points representing 3 face components (eyes, eyebrows and mouth). Then they generate the facial position information which is input to the input units of a neural network; the network learning is done by backpropagation algorithm and the recognition test is carried out. For the six basic facial expressions, the correct recognition ratio was found to be about 90%. This paper further investigates the method of recognizing the strength of the six basic facial expressions by a neural network. >

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. >

Changes in Facial Recognition Memory Across the Adult Life Span

Abstract: We found significant and similar associations of facial recognition memory performance with age using two different methodologies: signal detection (SD) and delayed nonmatching-to-sample (DNM). These data demonstrate that previously reported associations between age and facial recognition memory performance were not specific to method of assessment, and that significant declines are seen as early as 50 years of age.

Face Recognition by Computer

Abstract: We describe a coding scheme to index face images for subsequent retrieval, which seems effective, under some conditions, at coding the faces themselves, rather than particular face images, and uses typically 100 bytes. We report tests searching a pool of 100 faces, using as cue a different image of a face in the pool, taken 10 years later. In two of three tests with different faces, the target face best matches the corresponding cue.

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. >

Human face recognition method based on the statistical model of small sample size

Abstract: Automatic recognition of human faces is a frontier topic in computer vision. In this paper, a novel recognition approach to human faces is proposed, which is based on the statistical model in the optimal discriminant space. Singular value vector has been proposed to represent algebraic features of images. This kind of feature vector has some important properties of algebraic and geometric invariance, and insensitiveness to noise. Because singular value vector is usually of high dimensionality, and recognition model based on these feature vectors belongs to the problem of small sample size, which has not been solved completely, dimensionality compression of singular value vector is very necessary. In our method, an optimal discriminant transformation is constructed to transform an original space of singular value vector into a new space in which its dimensionality is significantly lower than that in the original space. Finally, a recognition model is established in the new space. Experimental results show that our method has very good recognition performance, and recognition accuracies of 100 percent are obtained for all 64 facial images of 8 classes of human faces.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Robust face identification scheme: KL expansion of an invariant feature space

Abstract: This paper proposes a new approach for extracting features from face images that offer robust face identification against image variations. We combine the K-L expansion technique with two new operations that transform the face pattern into an invariant feature space. The two operations are the affine transformation which yields a standard face view from the input face image, and its transformation into the Fourier spectrum domain, which develops the property of shift-invariance. Although the basic idea of applying the K-L expansion to extract features for face recognition originates from the eigenface approach proposed by Turk and Pentland our scheme offers superior performance due to the transformation into the invariant feature space. The performance of the two schemes for face identification against various imaging conditions is compared.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Face recognition impairments.

Abstract: Face recognition impairments are often found in the context of brain injury involving the right cerebral hemisphere. Recognition impairments can be dissociated from impairments affecting the processing of other types of information carried by the face, such as expression. The face recognition impairments themselves take different forms, corresponding to idealized stages or levels of recognition. These types of error can also arise as transitory phenomena in normal everyday life. From these observations, psychologists have proposed functional models that characterize the organization of the face processing system in schematic form. Such models provide useful ways of summarizing what is known. More importantly, they also allow new findings to act as tests of each model's usefulness by the extent to which they can be readily accommodated or force revision. Examples of this are briefly considered, including delusional misidentification, impaired learning of new faces, disordered attention to faces, 'covert' recognition in prosopagnosia, and unawareness of impaired face recognition.

Learning to Recognize Faces from Examples

Abstract: We describe an implemented system that learns to recognize human faces under varying pose and illumination conditions. The system relies on symmetry operations to detect the eyes and the mouth in a face image, uses the locations of these features to normalize the appearance of the face, performs simple but effective dimensionality reduction by a convolution with a set of Gaussian receptive fields, and subjects the vector of activities of the receptive fields to a Radial Basis Function interpolating classifier. The performance of the system compares favorably with the state of the art in machine recognition of faces.

Human face recognition from a single front view

Abstract: This paper presents a face recognition system which can identify the unknown identity effectively using the front-view facial features. In front-view facial feature extractions, we can capture the contours of eyes and mouth by the deformable template model because of their analytically describable shapes. However, the shapes of eyebrows, nostrils and face are difficult to model using a deformable template. We extract them by using the active contour model (snake). After the contours of all facial features have been captured, we calculate effective feature values from these extracted contours and construct databases for unknown identities classification. In the database generation phase, 12 models are photographed, and feature vectors are calculated for each portrait. In the identification phase if any one of these 12 persons has his picture taken again, the system can recognize his identity.

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. >

Face recognition from depth and curvature

Abstract: This thesis explores the representation of the human face by features based on the curvature of the face surface. Depth and curvature features have several advantages over more traditional intensity based features. Specifically curvature descriptors (1) have the potential for higher accuracy in describing surface based events, (2) are better suited to describe properties of the face in the area of the cheeks, forehead, and chin, and (3) are viewpoint invariant. Although several researchers have worked on the problem of interpreting range data from curved (although usually highly geometrically structured) surfaces, the main approaches have centered on segmentation of the data into simple surface types based on the sign of mean and Gaussian curvature. These types of methods are not sufficient to classify more natural smoothly curving objects, whose surfaces are not well modeled by piecewise planar, cylindrical, or spherical regions. This thesis details the calculation of principal curvature, the calculation of general surface descriptors based on curvature, and the calculation of face specific descriptors based both on curvature features and a priori knowledge about the structure of the face. These face specific descriptors can be incorporated into many different recognition strategies. We implement two such strategies, one based on depth template comparison and the other based on comparisons in feature space. Both systems show very promising experimental results.

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. >

Solving the Human Face Recognition Task using Neural Nets

Abstract: We describe in this paper some neural network architectures designed to identify human faces from a raster image. The proposed networks are based on a multi-layer perceptron with shared weights. We discuss different hybrid architectures, combining image feature extraction by MLP and classification by specialized algorithms such as LVQ, which offer robust performances and allow the system to detect “intruders”. We present the results of our architecture on large databases of varied complexities, containing images taken in real-life unconstrained conditions.

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. >

A face recognition system using fuzzy logic and artificial neural network

Abstract: The authors have developed a method to extract a feature vector that is very important to recognizing facial images. The eye blinking method was used to get the location of the eyes roughly. Then a feature vector was obtained using locations and distances between feature points, that is the eyes, the nose, the mouth and the outline of the face. To make the feature vector invariant to the size of the facial image, it was normalized. Fuzzy linguistic variables were used instead of real numbers to represent the approximate distance between feature points. These fuzzified feature vectors were learned by an artificial neural network and used to recognize a facial image in the recognition phase. The face recognizer could recognize all learned persons correctly in spite of variations. >

Neural nets for human face recognition

Abstract: A neural network (NN) architecture based on a multilayer perceptron with shared weights is described. This kind of network allows direct gray-level image processing and lets the NN learn to extract image features in its hidden layers. These features allow fast classification of face images. The results of applying the architecture on large databases of varying difficulty, containing images taken in real-life unconstrained condition, are presented. A novel rejection criterion which allows the system to detect intruders is discussed. >

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. >

Face recognition: combining cognitive psychology and image engineering

Abstract: Many cognitive tasks that are easy for humans to perform are proving difficult to emulate in computer systems. Combining the disciplines of psychology and engineering may offer a solution to some of these problems. A connectionist or neural network model of face recognition by humans which incorporates aspects of a model proposed by cognitive psychologists is presented. A comparative set of experiments has been performed using this simulation and human subjects for familiar face recognition. By employing the same stimuli for both humans and the computer model, it is possible to advance not only our understanding of human cognition but also to develop improved automated systems for face recognition

A facial image processing system for psychological studies

Abstract: This paper presents a facial image processing system which has been developed, based on the technology of intelligent (or knowledge based) image coding scheme proposed by Harashima et al. (1992). Moreover, the application of this system for psychological studies on human faces and facial expression are discussed with some examples. >