Showing papers on "Three-dimensional face recognition 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.

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.

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.

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 ...

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.

Object recognition robust under translations, deformations, and changes in background

Abstract: Recognition systems based on model matching using low level features often fail due to a variation in background. As a solution, I present a system for the recognition of human faces independent of hairstyle. Correspondence maps between an image and a model are established by coarse-fine matching in a Gabor pyramid. These are used for hierarchical recognition.

Motion-Based Recognition

Abstract: Motion-based recognition deals with the recognition of an object and/or its motion, based on motion in a series of images. In this approach, a sequence containing a large number of frames is used to extract motion information. The advantage is that a longer sequence leads to recognition of higher level motions, like walking or running, which consist of a complex and coordinated series of events. Unlike much previous research in motion, this approach does not require explicit reconstruction of shape from the images prior to recognition. This book provides the state-of-the-art in this rapidly developing discipline. It consists of a collection of invited chapters by leading researchers in the world covering various aspects of motion-based recognition including lipreading, gesture recognition, facial expression recognition, gait analysis, cyclic motion detection, and activity recognition. Audience: This volume will be of interest to researchers and post- graduate students whose work involves computer vision, robotics and image processing.

Face recognition using range images

Abstract: A system for face recognition using range images as input data is described. The range data acquisition procedure is based on the coded light approach, merging range images that are recorded by two separate sensors. Two approaches, which are known from face recognition based on grey level images have been extended to dealing with range images. These approaches are based on eigenfaces and hidden Markov models, respectively. Experimental results on a database with various range images from 24 persons show very promising results for both recognition methods.

A survey of face recognition algorithms and testing results

Abstract: Automated face recognition (AFR) has received increased attention We describe two general approaches to the problem and discuss their effectiveness and robustness with respect to several possible applications We also discuss some issues of run-time performance The AFR technology falls into three main subgroups, which represent more-or-less independent approaches to the problem: neural network solutions, eigenface solutions, and wavelet/elastic matching solutions Each of these first requires that a facial image be identified in a scene, a process called segmentation The image should be normalized to some extent Normalization is usually a combination of linear translation, rotation and scaling, although the elastic matching method includes spatial transformations

Robust real-time face tracking and gesture recognition

Abstract: People natural ly express themselves through facial gestures. We have implemented an interface that tracks a person's facial features robustly in real t ime (30Hz) and does not require art i f icial artifacts such as special i l lumination or facial makeup. Even if features become occluded the system is capable of recovering tracking in a couple of frames after the features reappear in the image. Based on this fault tolerant face tracker we have implemented realt ime gesture recognition capable of distinguish 12 different gestures ranging from "yes", "no" and "may be" to winks, blinks and "asleep".

Non-intrusive Person Authentication for Access Control by Visual Tracking and Face Recognition

Abstract: Face recognition systems typically operate robustly only within highly constrained environments. This paper describes work aimed at performing face recognition in more unconstrained environments such as occur in security applications based on closed-circuit television (CCTV). The system described detects and tracks several people as they move through complex scenes. It uses a single, fixed camera and extracts segmented face sequences which can be used to perform face recognition or verification. Example sequences are given to illustrate performance. An application in non-intrusive access control is discussed.

Face recognition with range images and intensity images

Abstract: The surface normal of a range image of a face can be decomposed into three components. The combinations of these three weighted components produce 2-D intensity images with different illuminations. A phase-only vector filter is designed based on these normal components. With such a vector filter, the face recognition can be performed between range face and range face, or between range face and intensity face. This kind of recognition is less sensitive to the changes of illumination of the input face.

Face Recognition: a Summary of 1995 - 1997

Abstract: The development of face recognition over the past years allows an organization into three types of r ecognition algorithms, namely frontal, profile, and v iew-tolerant recognition, depending on the kind o f imagery and the according recognition algorithms. While frontal recognition certainly is the c lassical approach, view-tolerant algorithms usually p erform recognition in a more sophisticated fashion by taking into consideration some of the underlying phy sics, geometry, and statistics. Profile schemes as s tand-alone systems have a rather marginal significance for identification. However, they are very p ractical either f or f ast coarse pre-searches of large face databases to reduce the c omputational l oad for a subsequent sophisticated algorithm, or as part of a hybrid recognition scheme. Such hyb rid approaches have a special status among face recognition systems as they combine different recognition approaches in an either serial or parallel order to ov ercome the shortcomings of the individual components.

Real-Time Detection of Human Faces in Uncontrolled Environments

Abstract: Mehdi KhosraviNCR Human Interface Technology CenterAtlanta, Georgia, 30309Monson H. HayesGeorgia Institute of Technology, Department of Electrical EngineeringAtlanta, Georgia, 30332ABSTRACTThis paper presents an approach for the detection of human face and eyes in real time and in uncontrolled environments.The system has been implemented on a PC platform with the aid of simple commercial devices such as an NTSC videocamera and a monochrome frame grabber. The approach is based on a probabilistic framework that uses a deformabletemplate model to describe the human face. The system has been tested on both head-and-shoulder sequences as well ascomplex scenes with multiple people and random motion. The system is able to locate the eyes from different head poses(rotations in image plane as well as in depth). The information provided by the location of the eyes is used to extract faceswith frontal pose from a video sequence. The extracted frontal frames can be passed to recognition and classificationsystems for further processing.Keywords : Face Detection, Eye Detection, Face Segmentation, Ellipse Fitting1. INTRODUCTIONIn recent years, face detection from video data has become a popular research area. There are numerous commercialapplications of face detection in face recognition, verification, classification, identification as well as security access andmultimedia. To extract the human faces in an uncontrolled environment most of these applications must deal with thedifficult problems of variations in lighting, variations in pose, occlusion of people by other people, and cluttered or non-uniform backgrounds.A review of the approaches to face detection that have been proposed are described in[1]. In [2], Sung and Poggio presentedan example-based learning approach for locating unoccluded human frontal faces. The approach measures a distancebetween the local image and a few view-based "face" and "non face" pattern prototypes at each image location to locate theface. In [3], Turk and Pentland used the distance to a "face space", defined by "eigenfaces", to locate and track frontalhuman faces. In [4], human faces were detected by searching for significant facial features at each location in the image. In[5]

New improved feature extraction methods for real-time high performance image sequence recognition

Abstract: This paper describes new feature extraction methods which can be used very effectively in combination with statistical methods for image sequence recognition. Although these feature extraction methods can be used for a wide variety of image sequence processing applications, the target application presented in this paper is gesture recognition. The novel feature extraction methods have been integrated into an HMM-based gesture recognition system and led to substantial improvements for this system. It turned out that the new features are not only able to describe the gesture characteristics much better than the old features, but additionally they also led to a dramatic reduction in dimensionality of the feature vector used for representing each frame of the image sequence. This resulted in the fact that it was possible to use the novel features in combination with a new architecture for statistical image sequence recognition. The result of this investigation is a high performance gesture recognition system with significantly improved recognition rates and real-time capabilities.

Audio- and Video-based Biometric Person Authentication: First International Conference, AVBPA '97, Crans-Montana, Switzerland, March 12 - 14, 1997, Proceedings

Abstract: Robust eye centre extraction using the Hough Transform.- Localising facial features with matched filters.- Shape normalisation for face recognition.- Generalized likelihood ratio-based face detection and extraction of mouth features.- Tracking facial feature points with Gabor wavelets and shape models.- Face detection by direct convexity estimation.- Analysis and encoding of lip movements.- Lip-shape dependent face verification.- Statistical chromaticity models for lip tracking with B-splines.- A fully automatic approach to facial feature detection and tracking.- Automatic Video-based Person Authentication using the RBF network.- Using gait as a biometric, via phase-weighted magnitude spectra.- Identity authentication using fingerprints.- An algorithm for recognising walkers.- Metrological remote identification of a human body by stereoscopic camera techniques.- Discriminant analysis for recognition of human face images.- Image representations for visual learning.- Automatic profile identification.- Recognition of facial images with low resolution using a Hopfield memory model.- Exclusion of photos and new segmentation algorithms for the automatic face recognition.- Face authentication using morphological dynamic link architecture.- Non-intrusive person authentication for access control by visual tracking and face recognition.- Profile authentication using a chamfer matching algorithm.- Subband approach for automatic speaker recognition: Optimal division of the frequency domain.- Optimizing feature set for speaker verification.- VQ score normalisation for text-dependent and text-independent speaker recognition.- A two stage procedure for phone based speaker verification.- Speech/speaker recognition using a HMM/GMM hybrid model.- Recent advances in speaker recognition.- Speaker identification using harmonic structure of LP-residual spectrum.- A speaker identification agent.- Text-independent speaker identification based on spectral weighting functions.- Parameter discrimination analysis in speaker identification using self organizing map.- Text independent speaker verification using multiple-state predictive neural networks.- "Watch these lips" - Adding to acoustic signals to improve speaker recognition.- Expert conciliation for multi modal person authentication systems by Bayesian statistics.- SESAM: A biometric person identification system using sensor fusion.- Person authentication by fusing face and speech information.- Acoustic-labial speaker verification.- Combining evidence in multimodal personal identity recognition systems.- A viseme-based approach to labiometrics for automatic lipreading.- Development of an audio-visual database system for human identification.- Video compression and person authentication.- Lock-control system using face identification.- A system for automatic face recognition.- Time Encoded Signal Processing and Recognition for reduced data, high performance Speaker Verification architectures.- The CAVE speaker verification project - Experiments on the YOHO and SESP corpora.- The FERET September 1996 database and evaluation procedure.- The M2VTS multimodal face database (Release 1.00).- One-shot 3D-shape and texture acquisition of facial data.- A multiple-baseline stereo for precise human face acquisition.- User perspectives on the security of access data, operator handover procedures and 'insult rate' for speaker verification in automated telephone services.- Integrating face recognition into security systems.

An affine coordinate based algorithm for reprojecting the human face for identification tasks

Abstract: We present an algorithm to generate new views of a human face, starting with at least two other views of the face. In a typical face recognition system, the task of comparison becomes easier if the faces have similar orientation with respect to the camera. The affine coordinate based reprojection algorithm presented enables us to do that. Dense point matches between the two input faces of the same individual are computed using an affine coordinate based reprojection framework. This is followed by the reprojection of one of these to faces to the target face once the user has matched four feature points across two input face images and the target face image.

Face recognition using statistical models

Abstract: We describe the use of flexible models for the representation of shape and grey-level appearance of human faces. The models are controlled by a small number of parameters, which can be used to code the overall appearance of a face for image compression and classification. Shape and grey-level appearance are included in a single model. Discriminant analysis allows the isolation of variation important for classification of identity. We have performed both face recognition and face synthesis experiments and present the results in this paper.

Detecting human face from monocular image sequences by genetic algorithms

Abstract: This work investigates a new approach to detect human face from monocular image sequences. Our method consists of two main search procedures, both using genetic algorithms. The first one is to find a head inside the scene and the second one is to identify the existence of face within the extracted head area. For this purpose, we have developed two models to be used as a tool to calculate the fitness for each observation in the search procedure: one is a head model which is approached by an ellipse and the other is a face template the size of which is adjustable. The procedures work sequentially. The head search is activated first, and after the head area is found, the face identification is activated. The experiment demonstrates the effectiveness of the method.

Dual eigenspace method for human face recognition

Abstract: The authors present an effective scheme called the dual eigenspace method (DEM) for automated face recognition. Based on the K-L transform, the dual eigenspaces are constructed by extracting algebraic features of training samples and applied to face identification with a two-layer minimium distance classifier. Experimental results show that DEM is significantly better than the traditional eigenface method (TEM).

Recognizing faces from a new viewpoint

Abstract: A new technique is described for recognizing faces from new viewpoints. From a single 2D image of a face synthetic images from new viewpoints are generated and compared to stored views. A novel 2D image of a face can be computed without knowledge about the 3D structure of the head. The technique draws on prior knowledge of faces based on example images of other faces seen in different poses and on a single generic 3D model of a human head. The example images are used to learn a pose-invariant shape and texture description of a new face. The 3D model is used to solve the correspondence problem between images showing faces in different poses. The performance of the technique is tested on a data set of 200 faces of known orientation for rotations up to 90/spl deg/.

Face and eye location algorithms for visual user interface

Abstract: Face image processing techniques are essential for advanced human-computer interaction. Most previous face image processing systems have been proposed under the assumption that approximative face regions or the locations of facial features are known. In this paper, we mainly discuss face and eye location algorithms. The proposed techniques use no prior knowledge on the given image. It is less sensitive to backgrounds and lighting conditions than previous approaches. Mosaic image representation and valley representation are adopted for face detection and eye location, respectively. Extensive experiments have been performed, and very encouraging results have been achieved.

Verification of faces in cluttered photographs

Abstract: The human face is an object that is easily located in complex scenes by infants and adults alike. Our objective is to verify face locations hypothesized in photographs such as the ones typified by those found in newspapers. Our approach to face verification is based on a methodology of a hierarchical rule-based system. The rules themselves are derived by a fuzzy model of assigning scores to the "goodness" of match between image features and model features. Scores of the match computed with rules are further refined by a relaxation process. Face candidates are generated by a face locator and include face images as well as non-face images. Our objective is to confirm the face images while rejecting the non-face images. In an experiment on 80 face candidates returned by a face locator applied to newspaper photographs, the face verifier correctly identified all the faces as faces with a false positive rate of about 10%.

Analysis and recognition of facial expression image

Abstract: Based on the image analysis of human facial expression, a model of facial image based on constituents is built for facial expression image recognition in this paper. The decision tree of facial expression image is proposed according to the features of particular constituents, such as the shape and position. The features of facial images are extracted by using template matching and optimization method.

Facial Feature Detection Using Neural Networks

Abstract: Many human-machine interfaces based on face gestures are strongly user-dependent. We want to overcome this limitation by using common facial features like eyes, nose and mouth for gaze recognition. In a first step an adaptive color histogram segmentation method roughly determines the region of interest including the user's face. Within this region we then use a hierarchical recognition approach to detect the facial features. Our system is based on a what-where neural network architecture and allows a fast and robust recognition rate. In the future we intend to use the conspicuous features for estimation of gaze directions.