Showing papers on "Facial recognition system published in 1997"

Eigenfaces vs. Fisherfaces: recognition using class specific linear projection

Abstract: We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space-if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed "Fisherface" method has error rates that are lower than those of the eigenface technique for tests on the Harvard and Yale face databases.

Face recognition: a convolutional neural-network approach

Abstract: We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.

Face recognition by elastic bunch graph matching

Abstract: We present a system for recognizing human faces from single images out of a large database containing one image per person. Faces are represented by labeled graphs, based on a Gabor wavelet transform. Image graphs of new faces are extracted by an elastic graph matching process and can be compared by a simple similarity function. The system differs from the preceding one (Lades et al., 1993) in three respects. Phase information is used for accurate node positioning. Object-adapted graphs are used to handle large rotations in depth. Image graph extraction is based on a novel data structure, the bunch graph, which is constructed from a small get of sample image graphs.

The FERET evaluation methodology for face-recognition algorithms

Abstract: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1199 individuals are included in the FERET database, which is divided into development and sequestered portions. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to (1) assess the state of the art, (2) identify future areas of research, and (3) measure algorithm performance on large databases.

Face recognition by elastic bunch graph matching

Abstract: We present a system for recognizing human faces from single images out of a large database containing one image per person. Faces are represented by labeled graphs, based on a Gabor wavelet transform. Image graphs of new faces are extracted by an elastic graph matching process and can be compared by a simple similarity function. The system differs from Lades et al. (1993) in three respects. Phase information is used for accurate node positioning. Object-adapted graphs are used to handle large rotations in depth. Image graph extraction is based on a novel data structure, the bunch graph, which is constructed from a small set of sample image graphs.

Face Recognition by Elastic Bunch Graph Matching

Abstract: We present a system for recognizing human faces from single images out of a large database with one image per person. The task is difficult because of image variation in terms of position, size, expression, and pose. The system collapses most of this variance by extracting concise face descriptions in the form of image graphs. In these, fiducial points on the face (eyes, mouth etc.) are described by sets of wavelet components (jets). Image graph extraction is based on a novel approach, the bunch graph, which is constructed from a small set of sample image graphs. Recognition is based on a straight-forward comparison of image graphs. We report recognition experiments on the FERET database and the Bochum database, including recognition across pose.

Face recognition: the problem of compensating for changes in illumination direction

Abstract: A face recognition system must recognize a face from a novel image despite the variations between images of the same face. A common approach to overcoming image variations because of changes in the illumination conditions is to use image representations that are relatively insensitive to these variations. Examples of such representations are edge maps, image intensity derivatives, and images convolved with 2D Gabor-like filters. Here we present an empirical study that evaluates the sensitivity of these representations to changes in illumination, as well as viewpoint and facial expression. Our findings indicated that none of the representations considered is sufficient by itself to overcome image variations because of a change in the direction of illumination. Similar results were obtained for changes due to viewpoint and expression. Image representations that emphasized the horizontal features were found to be less sensitive to changes in the direction of illumination. However, systems based only on such representations failed to recognize up to 20 percent of the faces in our database. Humans performed considerably better under the same conditions. We discuss possible reasons for this superiority and alternative methods for overcoming illumination effects in recognition.

Discriminant analysis for recognition of human face images

Abstract: In this paper the discriminatory power of various human facial features is studied and a new scheme for Automatic Face Recognition (AFR) is proposed. Using Linear Discriminant Analysis (LDA) of different aspects of human faces in spatial domain, we first evaluate the significance of visual information in different parts/features of the face for identifying the human subject. The LDA of faces also provides us with a small set of features that carry the most relevant information for classification purposes. The features are obtained through eigenvector analysis of scatter matrices with the objective of maximizing between-class and minimizing within-class variations. The result is an efficient projection-based feature extraction and classification scheme for AFR. Soft decisions made based on each of the projections are combined, using probabilistic or evidential approaches to multisource data analysis. For medium-sized databases of human faces, good classification accuracy is achieved using very low-dimensional feature vectors.

Coding, analysis, interpretation, and recognition of facial expressions

Abstract: We describe a computer vision system for observing facial motion by using an optimal estimation optical flow method coupled with geometric, physical and motion-based dynamic models describing the facial structure. Our method produces a reliable parametric representation of the face's independent muscle action groups, as well as an accurate estimate of facial motion. Previous efforts at analysis of facial expression have been based on the facial action coding system (FACS), a representation developed in order to allow human psychologists to code expression from static pictures. To avoid use of this heuristic coding scheme, we have used our computer vision system to probabilistically characterize facial motion and muscle activation in an experimental population, thus deriving a new, more accurate, representation of human facial expressions that we call FACS+. Finally, we show how this method can be used for coding, analysis, interpretation, and recognition of facial expressions.

Automatic interpretation and coding of face images using flexible models

Abstract: Face images are difficult to interpret because they are highly variable. Sources of variability include individual appearance, 3D pose, facial expression, and lighting. We describe a compact parametrized model of facial appearance which takes into account all these sources of variability. The model represents both shape and gray-level appearance, and is created by performing a statistical analysis over a training set of face images. A robust multiresolution search algorithm is used to fit the model to faces in new images. This allows the main facial features to be located, and a set of shape, and gray-level appearance parameters to be recovered. A good approximation to a given face can be reconstructed using less than 100 of these parameters. This representation can be used for tasks such as image coding, person identification, 3D pose recovery, gender recognition, and expression recognition. Experimental results are presented for a database of 690 face images obtained under widely varying conditions of 3D pose, lighting, and facial expression. The system performs well on all the tasks listed above.

Face recognition/detection by probabilistic decision-based neural network

Abstract: This paper proposes a face recognition system, based on probabilistic decision-based neural networks (PDBNN). With technological advance on microelectronic and vision system, high performance automatic techniques on biometric recognition are now becoming economically feasible. Among all the biometric identification methods, face recognition has attracted much attention in recent years because it has potential to be most nonintrusive and user-friendly. The PDBNN face recognition system consists of three modules: First, a face detector finds the location of a human face in an image. Then an eye localizer determines the positions of both eyes in order to generate meaningful feature vectors. The facial region proposed contains eyebrows, eyes, and nose, but excluding mouth (eye-glasses will be allowed). Lastly, the third module is a face recognizer. The PDBNN can be effectively applied to all the three modules. It adopts a hierarchical network structures with nonlinear basis functions and a competitive credit-assignment scheme. The paper demonstrates a successful application of PDBNN to face recognition applications on two public (FERET and ORL) and one in-house (SCR) databases. Regarding the performance, experimental results on three different databases such as recognition accuracies as well as false rejection and false acceptance rates are elaborated. As to the processing speed, the whole recognition process (including PDBNN processing for eye localization, feature extraction, and classification) consumes approximately one second on Sparc10, without using hardware accelerator or co-processor.

What is special about face recognition? nineteen experiments on a person with visual object agnosia and dyslexia but normal face recognition

Abstract: In order to study face recognition in relative isolation from visual processes that may also contribute to object recognition and reading, we investigated CK, a man with normal face recognition but with object agnosia and dyslexia caused by a closed-head injury. We administered recognition tests of up right faces, of family resemblance, of age-transformed faces, of caricatures, of cartoons, of inverted faces, and of face features, of disguised faces, of perceptually degraded faces, of fractured faces, of faces parts, and of faces whose parts were made of objects. We compared CK's performance with that of at least 12 control participants. We found that CK performed as well as controls as long as the face was upright and retained the configurational integrity among the internal facial features, the eyes, nose, and mouth. This held regardless of whether the face was disguised or degraded and whether the face was represented as a photo, a caricature, a cartoon, or a face composed of objects. In the last case, CK perceived the face but, unlike controls, was rarely aware that it was composed of objects. When the face, or just the internal features, were inverted or when the configurational gestalt was broken by fracturing the face or misaligning the top and bottom halves, CK's performance suffered far more than that of controls. We conclude that face recognition normally depends on two systems: (1) a holistic, face-specific system that is dependent on orientationspecific coding of second-order relational features (internal), which is intact in CK and (2) a part-based object-recognition system, which is damaged in CK and which contributes to face recognition when the face stimulus does not satisfy the domain-specific conditions needed to activate the face system.

Features and their configuration in face recognition

Abstract: Tanaka and Farah (1993) have proposed a holistic approach to face recognition in which information about the features of a face and their configuration are combined together in the face representation. An implication of the holistic hypothesis is that alterations in facial configuration should interfere with retrieval of features. In four experiments, the effect of configuration on feature recognition was investigated by creating two configurations of a face, one with eyes close together and one with eyes far apart. After subjects studied faces presented in one of the two configurations (eyes-close or eyes-far), they were tested for their recognition of features shown in isolation, in a new face configuration, and in the old face configuration. It was found that subjects recognized features best when presented in the old configuration, next best in the new configuration, and poorest in isolation. Moreover, subjects were not sensitive to configural information in inverted faces (Experiment 2) or nonface stimuli (i.e., houses; Experiments 3 and 4). Importantly, for normal faces, altering the spatial location of the eyes not only impaired subjects’ recognition of the eye features but also impaired their recognition of the nose and mouth features—features whose spatial locations were not directly altered. These findings emphasize the interdependency of featural and configural information in a holistic face representation.

Face recognition : Eigenface, elastic matching, and neural nets : Automated biometrics

Abstract: This paper is a comparative study of three recently proposed algorithms for face recognition: eigenface, autoassociation and classification neural nets, and elastic matching. After these algorithms were analyzed under a common statistical decision framework, they were evaluated experimentally on four individual data bases, each with a moderate subject size, and a combined data base with more than a hundred different subjects. Analysis and experimental results indicate that the eigenface algorithm, which is essentially a minimum distance classifier, works well when lighting variation is small. Its performance deteriorates significantly as lighting variation increases. The elastic matching algorithm, on the other hand, is insensitive to lighting, face position, and expression variations and therefore is more versatile. The performance of the autoassociation and classification nets is upper bounded by that of the eigenface but is more difficult to implement in practice.

Player tracking and identification system

Abstract: A system and method is set forth for passively tracking the play of players playing gaming devices such as slot machines. Players provide identification information and facial recognition data is acquired as by a digital or video camera. For each player an account file and a file of the facial image data is stored. When the player plays the slot machine, a camera scans the player and acquires facial image data which is compared to stored data to identify the player. The identified player's account file is opened and data from the device representing parameters of play, e.g. amounts wagered is allocated to the identified player's account file for the purpose of providing comps and other benefits to the player. Doe image data and account files can be stored to allocate parameters for unidentified players. Further the device acquired image data can be compared with stored image data to identify undesirables such as slot cheats or the like.

Face recognition: eigenface, elastic matching, and neural nets

Abstract: This paper is a comparative study of three recently proposed algorithms for face recognition: eigenface, autoassociation and classification neural nets, and elastic matching. After these algorithms were analyzed under a common statistical decision framework, they were evaluated experimentally on four individual data bases, each with a moderate subject size, and a combined data base with more than a hundred different subjects. Analysis and experimental results indicate that the eigenface algorithm, which is essentially a minimum distance classifier, works well when lighting variation is small. Its performance deteriorates significantly as lighting variation increases. The elastic matching algorithm, on the other hand, is insensitive to lighting, face position, and expression variations and therefore is more versatile. The performance of the autoassociation and classification nets is upper bounded by that of the eigenface but is more difficult to implement in practice.

Parametrized structure from motion for 3D adaptive feedback tracking of faces

Abstract: A real-time system is described for automatically detecting, modeling and tracking faces in 3D. A closed loop approach is proposed which utilizes structure from motion to generate a 3D model of a face and then feed back the estimated structure to constrain feature tracking in the next frame. The system initializes by using skin classification, symmetry operations, 3D warping and eigenfaces to find a face. Feature trajectories are then computed by SSD or correlation-based tracking. The trajectories are simultaneously processed by an extended Kalman filter to stably recover 3D structure, camera geometry and facial pose. Adaptively weighted estimation is used in this filter by modeling the noise characteristics of the 2D image patch tracking technique. In addition, the structural estimate is constrained by using parametrized models of facial structure (eigen-heads). The Kalman filter's estimate of the 3D state and motion of the face predicts the trajectory of the features which constrains the search space for the next frame in the video sequence. The feature tracking and Kalman filtering closed loop system operates at 25 Hz.

Facial emotion recognition using multi-modal information

Abstract: Facial emotion recognition will become vitally important in future multi-cultural visual communication systems, for emotion translation between cultures, which may be considered analogous to speech translation. However so far the recognition of facial emotions is mainly addressed by computer vision researchers, based on facial display. Also detection of vocal expressions of emotions can be found in research work done by acoustic researchers. Most of these research paradigms are devoted purely to visual or purely to auditory human emotion detection. However we found that it is very interesting to consider both these auditory and visual information together, for processing, since we hope this kind of multi-modal information processing will become a datum of information processing in future multimedia era. By several intensive subjective evaluation studies we found that human beings recognise anger, happiness, surprise and dislike by their visual appearance, compared to voice only detection. When the audio track of each emotion clip is dubbed with a different type of auditory emotional expression, still anger, happiness and surprise were video dominant. However the dislike emotion gave mixed responses to different speakers. In both studies we found that sadness and fear emotions were audio dominant. As a conclusion, we propose a method of facial emotion detection by using a hybrid approach, which uses multi-modal information for facial emotion recognition.

Multi-modal tracking of faces for video communications

Abstract: Visual processes to detect and track faces for video compression and transmission. The system is based on an architecture in which a supervisor selects and activates visual processes in cyclic manner. Control of visual processes is made possible by a confidence factor which accompanies each observation. Fusion of results into a unified estimation for tracking is made possible by estimating a covariance matrix with each observation. Visual processes for face tracking are described using blink detection, normalised color histogram matching, and cross correlation (SSD and NCC). Ensembles of visual processes are organised into processing states so as to provide robust tracking. Transition between states is determined by events detected by processes. The result of face detection is fed into recursive estimator (Kalman filter). The output from the estimator drives a PD controller for a pan/tilt/zoom camera. The resulting system provides robust and precise tracking which operates continuously at approximately 20 images per second on a 150 megahertz computer workstation.

The Role of Movement in Face Recognition

Abstract: The movement of the face may provide information that facilitates recognition. However, in mostsituations people who are very familiar to us can be recognized easily from a single typical view of the face and the presence of further information derived from movement would not be expected to improve performance. Here the effects of movement on face recognition are investigated for faces presented under non-optimal conditions. Subjects were required to identify moving or still videotaped faces of famous and unknown people. Faces were presented in negative, a manipulation which preserved the two-dimensional shape and configuration of the face and facial features, while degrading face recognition performance. Results indicated that moving faces were significantly better recognized than still faces. It was proposed that movement may provide evidence about the three-dimensional structure of the face and allow the recognition of characteristic facial gestures. When the faces were inverted, no significant effect ...

Neurocomputational bases of object and face recognition

Abstract: A number of behavioural phenomena distinguish the recognition of faces and objects, even when members of a set of objects are highly similar. Because faces have the same parts in approximately the same relations, individuation of faces typically requires specification of the metric variation in a holistic and integral representation of the facial surface. The direct mapping of a hypercolumn-like pattern of activation onto a representation layer that preserves relative spatial filter values in a two-dimensional (2D) coordinate space, as proposed by C. von der Malsburg and his associates, may account for many of the phenomena associated with face recognition. An additional refinement, in which each column of filters (termed a 'jet') is centred on a particular facial feature (or fiducial point), allows selectivity of the input into the holistic representation to avoid incorporation of occluding or nearby surfaces. The initial hypercolumn representation also characterizes the first stage of object perception, but the image variation for objects at a given location in a 2D coordinate space may be too great to yield sufficient predictability directly from the output of spatial kernels. Consequently, objects can be represented by a structural description specifying qualitative (typically, non-accidental) characterizations of an object's parts, the attributes of the parts, and the relations among the parts, largely based on orientation and depth discontinuities (as shown by Hummel & Biederman). A series of experiments on the name priming or physical matching of complementary images (in the Fourier domain) of objects and faces documents that whereas face recognition is strongly dependent on the original spatial filter values, evidence from object recognition indicates strong invariance to these values, even when distinguishing among objects that are as similar as faces.

LAFTER: lips and face real time tracker

Abstract: This paper describes an active-camera real-time system for tracking, shape description, and classification of the human face and mouth using only an SGI Indy computer. The system is based on use of 2-D blob features, which are spatially-compact clusters of pixels that are similar in terms of low-level image properties. Patterns of behavior (e.g., facial expressions and head movements) can be classified in real-time using Hidden Markov Model (HMM) methods. The system has been tested on hundreds of users and has demonstrated extremely reliable and accurate performance. Typical classification accuracies are near 100%.

Rule-based face detection in frontal views

Abstract: Face detection is a key problem in building automated systems that perform face recognition A very attractive approach for face detection is based on multiresolution images (also known as mosaic images) Motivated by the simplicity of this approach, a rule-based face detection algorithm in frontal views is developed that extends the work of G Yang and TS Huang (see Pattern Recognition, vol27, no1, p53-63, 1994) The proposed algorithm has been applied to frontal views extracted from the European ACTS M2VTS database that contains the videosequences of 37 different persons It has been found that the algorithm provides a correct facial candidate in all cases However, the success rate of the detected facial features (eg eyebrows/eyes, nostrils/nose, and mouth) that validate the choice of a facial candidate is found to be 865% under the most strict evaluation conditions

Face and gesture recognition: overview

Abstract: Computerised recognition of faces and facial expressions would be useful for human-computer interface, and provision for facial animation is to be included in the ISO standard MPEG-4 by 1999. This could also be used for face image compression. The technology could be used for personal identification, and would be proof against fraud. Degrees of difference between people are discussed, with particular regard to identical twins. A particularly good feature for personal identification is the texture of the iris. A problem is that there is more difference between images of the same face with, e.g., different expression or illumination, than there sometimes is between images of different faces. Face recognition by the brain is discussed.

Recognizing moving faces: The relative contribution of motion and perspective view information.

Abstract: Four experiments are reported that investigated the effects of motion on face recognition by comparing the recognition of moving, multiple static, and single static images of faces. The results of these experiments show that moving faces can be recognized more accurately than static faces, and this appears to be the case even if static cues can be employed to produce accurate recognition rates. The distinction between motion and perspective view information was investigated by manipulating the number and order in which multiple views of the same face were presented. The results obtained in these experiments suggest that the recognition advantage found for moving faces is not simply a product of the different angles of view which are contained in a moving face. Motion therefore appears to be an important source of information in recognizing a face, and probably aids in the derivation of 3-D structure.

Face detection with information-based maximum discrimination

Abstract: In this paper we present a visual learning technique that maximizes the discrimination between positive and negative examples in a training set. We demonstrate our technique in the context of face detection with complex background without color or motion information, which has proven to be a challenging problem. We use a family of discrete Markov processes to model the face and background patterns and estimate the probability models using the data statistics. Then, we convert the learning process into an optimization, selecting the Markov process that optimizes the information-based discrimination between the two classes. The detection process is carried out by computing the likelihood ratio using the probability model obtained from the learning procedure. We show that because of the discrete nature of these models, the detection process is at least two orders of magnitude less computationally expensive than neural network approaches. However, no improvement in terms of correct-answer/false-alarm tradeoff is achieved.

Expertise in Object and Face Recognition

Abstract: This chapter defines object expertise as the ability to recognize objects at the subordinate level of abstraction Following this criterion, object experts, such as bird and dog experts, demonstrated a downward shift in recognition when identifying objects from their domain of expertise and normal adults when identifying faces However, the functional equivalence of expert object recognition and face recognition does not imply that they are mediated by a similar mechanism The chapter adopts a computational approach to the specialness question Face recognition researchers suggests that faces, unlike other objects, are recognized holistically-a process that is operationalized as difference in recognition of part when presented in isolation and in the whole object Thus, it discusses whether holistic recognition is a face specific computation or whether it is a general form of expert object recognition The discussion, addresses the separate questions of uniqueness and expertise as they relate to the studies with real-world and laboratory experts

Integrated vehicle security system utilizing facial image verification

Abstract: A method and apparatus for preventing theft of, and/or facilitating authorized access to, automotive vehicles generally comprises an image acquisition device adapted to generate signals representative of a human facial image wherein a processor associated with the image acquisition device is adapted to operatively receive the signals and generate an output relative to recognition or non-recognition of the human facial image. A response interface is associated with the processor and adapted to effect a vehicle security measure responsive to the recognition or non-recognition of the human facial image. An enrollment interface is adapted for enrolling authorized human users. The processor is adapted to compare signals generated by the image acquisition device with stored images of authorized users, generally by a face recognition engine which may be implemented with either a neural network or principal component analysis or their equivalent. Processing by the face recognition engine is facilitated by providing a morphological pre-processor which may screen images for quality or, in at least one embodiment, perform some verification functions. A postprocessor may be provided to make the determination of recognition or non-recognition based upon a predetermined threshold value of recognition. A triggering event interface is provided for communicating to the system the existence of those conditions necessitating verification of the user. Such events may include the opening of a car door, attempts to start the vehicle or attempts to access the vehicle. A response interface is also provided for effecting appropriate vehicle security measures. The response interface is generally one or more interconnections to the vehicle's microprocessor, door lock relay or alarm system. This interface will function to disable operation of the vehicle and/or sound the alarm in the case of attempted unauthorized use or access and will also serve to facilitate access to the vehicle in the case of authorized use.