Showing papers on "Facial recognition system published in 2003"

Face recognition: A literature survey

Abstract: As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.

Face recognition based on fitting a 3D morphable model

Abstract: This paper presents a method for face recognition across variations in pose, ranging from frontal to profile views, and across a wide range of illuminations, including cast shadows and specular reflections. To account for these variations, the algorithm simulates the process of image formation in 3D space, using computer graphics, and it estimates 3D shape and texture of faces from single images. The estimate is achieved by fitting a statistical, morphable model of 3D faces to images. The model is learned from a set of textured 3D scans of heads. We describe the construction of the morphable model, an algorithm to fit the model to images, and a framework for face identification. In this framework, faces are represented by model parameters for 3D shape and texture. We present results obtained with 4,488 images from the publicly available CMU-PIE database and 1,940 images from the FERET database.

The CMU pose, illumination, and expression database

Abstract: In the Fall of 2000, we collected a database of more than 40,000 facial images of 68 people. Using the Carnegie Mellon University 3D Room, we imaged each person across 13 different poses, under 43 different illumination conditions, and with four different expressions. We call this the CMU pose, illumination, and expression (PIE) database. We describe the imaging hardware, the collection procedure, the organization of the images, several possible uses, and how to obtain the database.

Face recognition using LDA-based algorithms

Abstract: Low-dimensional feature representation with enhanced discriminatory power is of paramount importance to face recognition (FR) systems. Most of traditional linear discriminant analysis (LDA)-based methods suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the "small sample size" (SSS) problem which is often encountered in FR tasks. In this paper, we propose a new algorithm that deals with both of the shortcomings in an efficient and cost effective manner. The proposed method is compared, in terms of classification accuracy, to other commonly used FR methods on two face databases. Results indicate that the performance of the proposed method is overall superior to those of traditional FR approaches, such as the eigenfaces, fisherfaces, and D-LDA methods.

Face recognition using kernel direct discriminant analysis algorithms

Abstract: Techniques that can introduce low-dimensional feature representation with enhanced discriminatory power is of paramount importance in face recognition (FR) systems. It is well known that the distribution of face images, under a perceivable variation in viewpoint, illumination or facial expression, is highly nonlinear and complex. It is, therefore, not surprising that linear techniques, such as those based on principle component analysis (PCA) or linear discriminant analysis (LDA), cannot provide reliable and robust solutions to those FR problems with complex face variations. In this paper, we propose a kernel machine-based discriminant analysis method, which deals with the nonlinearity of the face patterns' distribution. The proposed method also effectively solves the so-called "small sample size" (SSS) problem, which exists in most FR tasks. The new algorithm has been tested, in terms of classification error rate performance, on the multiview UMIST face database. Results indicate that the proposed methodology is able to achieve excellent performance with only a very small set of features being used, and its error rate is approximately 34% and 48% of those of two other commonly used kernel FR approaches, the kernel-PCA (KPCA) and the generalized discriminant analysis (GDA), respectively.

Comparison and combination of ear and face images in appearance-based biometrics

Abstract: Researchers have suggested that the ear may have advantages over the face for biometric recognition. Our previous experiments with ear and face recognition, using the standard principal component analysis approach, showed lower recognition performance using ear images. We report results of similar experiments on larger data sets that are more rigorously controlled for relative quality of face and ear images. We find that recognition performance is not significantly different between the face and the ear, for example, 70.5 percent versus 71.6 percent, respectively, in one experiment. We also find that multimodal recognition using both the ear and face results in statistically significant improvement over either individual biometric, for example, 90.9 percent in the analogous experiment.

Subject independent facial expression recognition with robust face detection using a convolutional neural network

Abstract: Reliable detection of ordinary facial expressions (e.g. smile) despite the variability among individuals as well as face appearance is an important step toward the realization of perceptual user interface with autonomous perception of persons. We describe a rule-based algorithm for robust facial expression recognition combined with robust face detection using a convolutional neural network. In this study, we address the problem of subject independence as well as translation, rotation, and scale invariance in the recognition of facial expression. The result shows reliable detection of smiles with recognition rate of 97.6% for 5600 still images of more than 10 subjects. The proposed algorithm demonstrated the ability to discriminate smiling from talking based on the saliency score obtained from voting visual cues. To the best of our knowledge, it is the first facial expression recognition model with the property of subject independence combined with robustness to variability in facial appearance.

Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction.

Abstract: Computer animated agents and robots bring a social dimension to human computer interaction and force us to think in new ways about how computers could be used in daily life. Face to face communication is a real-time process operating at a a time scale in the order of 40 milliseconds. The level of uncertainty at this time scale is considerable, making it necessary for humans and machines to rely on sensory rich perceptual primitives rather than slow symbolic inference processes. In this paper we present progress on one such perceptual primitive. The system automatically detects frontal faces in the video stream and codes them with respect to 7 dimensions in real time: neutral, anger, disgust, fear, joy, sadness, surprise. The face finder employs a cascade of feature detectors trained with boosting techniques [15, 2]. The expression recognizer receives image patches located by the face detector. A Gabor representation of the patch is formed and then processed by a bank of SVM classifiers. A novel combination of Adaboost and SVM's enhances performance. The system was tested on the Cohn-Kanade dataset of posed facial expressions [6]. The generalization performance to new subjects for a 7- way forced choice correct. Most interestingly the outputs of the classifier change smoothly as a function of time, providing a potentially valuable representation to code facial expression dynamics in a fully automatic and unobtrusive manner. The system has been deployed on a wide variety of platforms including Sony's Aibo pet robot, ATR's RoboVie, and CU animator, and is currently being evaluated for applications including automatic reading tutors, assessment of human-robot interaction.

Video-based face recognition using probabilistic appearance manifolds

Abstract: This paper presents a method to model and recognize human faces in video sequences. Each registered person is represented by a low-dimensional appearance manifold in the ambient image space, the complex nonlinear appearance manifold expressed as a collection of subsets (named pose manifolds), and the connectivity among them. Each pose manifold is approximated by an affine plane. To construct this representation, exemplars are sampled from videos, and these exemplars are clustered with a K-means algorithm; each cluster is represented as a plane computed through principal component analysis (PCA). The connectivity between the pose manifolds encodes the transition probability between images in each of the pose manifold and is learned from a training video sequences. A maximum a posteriori formulation is presented for face recognition in test video sequences by integrating the likelihood that the input image comes from a particular pose manifold and the transition probability to this pose manifold from the previous frame. To recognize faces with partial occlusion, we introduce a weight mask into the process. Extensive experiments demonstrate that the proposed algorithm outperforms existing frame-based face recognition methods with temporal voting schemes.

Independent component analysis of Gabor features for face recognition

Abstract: We present an independent Gabor features (IGFs) method and its application to face recognition. The novelty of the IGF method comes from 1) the derivation of independent Gabor features in the feature extraction stage and 2) the development of an IGF features-based probabilistic reasoning model (PRM) classification method in the pattern recognition stage. In particular, the IGF method first derives a Gabor feature vector from a set of downsampled Gabor wavelet representations of face images, then reduces the dimensionality of the vector by means of principal component analysis, and finally defines the independent Gabor features based on the independent component analysis (ICA). The independence property of these Gabor features facilitates the application of the PRM method for classification. The rationale behind integrating the Gabor wavelets and the ICA is twofold. On the one hand, the Gabor transformed face images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. These images can, thus, produce salient local features that are most suitable for face recognition. On the other hand, ICA would further reduce redundancy and represent independent features explicitly. These independent features are most useful for subsequent pattern discrimination and associative recall. Experiments on face recognition using the FacE REcognition Technology (FERET) and the ORL datasets, where the images vary in illumination, expression, pose, and scale, show the feasibility of the IGF method. In particular, the IGF method achieves 98.5% correct face recognition accuracy when using 180 features for the FERET dataset, and 100% accuracy for the ORL dataset using 88 features.

Multilinear subspace analysis of image ensembles

Abstract: Multilinear algebra, the algebra of higher-order tensors, offers a potent mathematical framework for analyzing ensembles of images resulting from the interaction of any number of underlying factors. We present a dimensionality reduction algorithm that enables subspace analysis within the multilinear framework. This N-mode orthogonal iteration algorithm is based on a tensor decomposition known as the N-mode SVD, the natural extension to tensors of the conventional matrix singular value decomposition (SVD). We demonstrate the power of multilinear subspace analysis in the context of facial image ensembles, where the relevant factors include different faces, expressions, viewpoints, and illuminations. In prior work we showed that our multilinear representation, called TensorFaces, yields superior facial recognition rates relative to standard, linear (PCA/eigenfaces) approaches. We demonstrate factor-specific dimensionality reduction of facial image ensembles. For example, we can suppress illumination effects (shadows, highlights) while preserving detailed facial features, yielding a low perceptual error.

Face recognition vendor test 2002

Abstract: Summary form only given. The face recognition vendor test (FRVT) 2002 is an independently administered technology evaluation of mature face recognition systems. FRVT 2002 provides performance measures for assessing the capability of face recognition systems to meet requirements for large-scale, real-world applications. Participation in FRVT 2002 was open to commercial and mature prototype systems from universities, research institutes, and companies. Ten companies submitted either commercial or prototype systems. FRVT 2002 computed performance statistics on an extremely large data set-121,589 operational facial images of 37,437 individuals. FRVT 2002 1) characterized identification and watch list performance as a function of database size, 2) estimated the variability in performance for different groups of people, 3) characterized performance as a function of elapsed time between enrolled and new images of a person, and 4) investigated the effect of demographics on performance. FRVT 2002 showed that recognition from indoor images has made substantial progress since FRVT 2000. Demographic results show that males are easier to recognize than females and that older people are easier to recognize than younger people. FRVT 2002 also assessed the impact of three new techniques for improving face recognition: three-dimensional morphable models, normalization of similarity scores, and face recognition from video sequences. Results show that three-dimensional morphable models and normalization increase performance and that face recognition from video sequences offers only a limited increase in performance over still images. A new XML-based evaluation protocol was developed for FRVT 2002. This protocol is flexible and supports evaluations of biometrics in general The FRVT 2002 reports can be found at http://www.frvt.org.

Illumination normalization for robust face recognition against varying lighting conditions

Abstract: Evaluations of the state-of-the-art of both academic face recognition algorithms and commercial systems have shown that recognition performance of most current technologies degrades due to the variations of illumination. We investigate several illumination normalization methods and propose some novel solutions. The main contribution includes: (1) A gamma intensity correction (GIC) method is proposed to normalize the overall image intensity at the given illumination level; (2) A region-based strategy combining GIC and the histogram equalization (HE) is proposed to further eliminate the side-lighting effect; (3) A quotient illumination relighting (QIR) method is presented to synthesize images under a predefined normal lighting condition from the provided face images captured under nonnormal lighting condition. These methods are evaluated and compared on the Yale illumination face database B and Harvard illumination face database. Considerable improvements are observed. Some conclusions are given at last.

An image preprocessing algorithm for illumination invariant face recognition

Abstract: Face recognition algorithms have to deal with significant amounts of illumination variations between gallery and probe images. State-of-the-art commercial face recognition algorithms still struggle with this problem. We propose a new image preprocessing algorithm that compensates for illumination variations in images. From a single brightness image the algorithm first estimates the illumination field and then compensates for it to mostly recover the scene reflectance. Unlike previously proposed approaches for illumination compensation, our algorithm does not require any training steps, knowledge of 3D face models or reflective surface models. We apply the algorithm to face images prior to recognition. We demonstrate large performance improvements with several standard face recognition algorithms across multiple, publicly available face databases.

Face recognition in hyperspectral images

Abstract: Hyperspectral cameras provide useful discriminants for human face recognition that cannot be obtained by other imaging methods. We examine the utility of using near-infrared hyperspectral images for the recognition of faces over a database of 200 subjects. The hyperspectral images were collected using a CCD camera equipped with a liquid crystal tunable filter to provide 31 bands over the near-infrared (0.7 /spl mu/m-1.0 /spl mu/m). Spectral measurements over the near-infrared allow the sensing of subsurface tissue structure which is significantly different from person to person, but relatively stable over time. The local spectral properties of human tissue are nearly invariant to face orientation and expression which allows hyperspectral discriminants to be used for recognition over a large range of poses and expressions. We describe a face recognition algorithm that exploits spectral measurements for multiple facial tissue types. We demonstrate experimentally that this algorithm can be used to recognize faces over time in the presence of changes in facial pose and expression.

Component-based face recognition with 3D morphable models

Abstract: We present a novel approach to pose and illumination invariant face recognition that combines two recent advances in the computer vision field: component-based recognition and 3D morphable models. First, a 3D morphable model is used to generate 3D face models from three input images from each person in the training database. The 3D models are rendered under varying pose and illumination conditions to build a large set of synthetic images. These images are then used to train a component-based face recognition system. The resulting system achieved 90% accuracy on a database of 1200 real images of six people and significantly outperformed a comparable global face recognition system. The results show the potential of the combination of morphable models and component-based recognition towards pose and illumination invariant face recognition based on only three training images of each subject.

Eigenface-domain super-resolution for face recognition

Abstract: Face images that are captured by surveillance cameras usually have a very low resolution, which significantly limits the performance of face recognition systems. In the past, super-resolution techniques have been proposed to increase the resolution by combining information from multiple images. These techniques use super-resolution as a preprocessing step to obtain a high-resolution image that is later passed to a face recognition system. Considering that most state-of-the-art face recognition systems use an initial dimensionality reduction method, we propose to transfer the super-resolution reconstruction from pixel domain to a lower dimensional face space. Such an approach has the advantage of a significant decrease in the computational complexity of the super-resolution reconstruction. The reconstruction algorithm no longer tries to obtain a visually improved high-quality image, but instead constructs the information required by the recognition system directly in the low dimensional domain without any unnecessary overhead. In addition, we show that face-space super-resolution is more robust to registration errors and noise than pixel-domain super-resolution because of the addition of model-based constraints.

Digital processing of video images

Abstract: Digital video imaging systems and techniques for efficiently transforming warped video images into rectilinear video images, real-time tracking of persons and objects, face recognition of persons, monitoring and tracking head pose of a person and associated perspective view of the person.

Method for Incorporating Facial Recognition Technology in a Multimedia Surveillance System

Abstract: Embodiment provide a surveillance camera adapted to be connected to an internet protocol network, the surveillance camera including at least one facial processor, at least one facial recognition algorithm embodied in suitable media, at least one facial recognition algorithm executable with digital format image data by at least one facial processor detecting faces, execution of at least one facial recognition algorithm producing unique facial image data, execution of at least one facial separation algorithm producing facial separation data, at least one facial processor in communication with at least one facial signature database to obtain reference data, execution of at least one facial signature algorithm comparing facial separation data and reference data to identify correlations, at least one compression algorithm producing compressed image data, and a network stack configured to transmit to the network unique facial image data for each detected face and compressed image data.

Image parsing: unifying segmentation, detection, and recognition

Abstract: We propose a general framework for parsing images into regions and objects. In this framework, the detection and recognition of objects proceed simultaneously with image segmentation in a competitive and cooperative manner. We illustrate our approach on natural images of complex city scenes where the objects of primary interest are faces and text. This method makes use of bottom-up proposals combined with top-down generative models using the data driven Markov chain Monte Carlo (DDMCMC) algorithm, which is guaranteed to converge to the optimal estimate asymptotically. More precisely, we define generative models for faces, text, and generic regions- e.g. shading, texture, and clutter. These models are activated by bottom-up proposals. The proposals for faces and text are learnt using a probabilistic version of AdaBoost. The DDMCMC combines reversible jump and diffusion dynamics to enable the generative models to explain the input images in a competitive and cooperative manner. Our experiments illustrate the advantages and importance of combining bottom-up and top-down models and of performing segmentation and object detection/recognition simultaneously.

Face Recognition Vendor Test 2002: Evaluation Report

Abstract: Abstract : The biggest change in the facial recognition community since the completion of the FERET program has been the introduction of facial recognition products to the commercial market. Open market competitiveness has driven numerous technological advances in automated face recognition since the FERET program and significantly lowered system costs. Today there are dozens of facial recognition systems available that have the potential to meet performance requirements for numerous applications. But which of these systems best meet the performance requirements for given applications? Repeated inquiries from numerous government agencies on the current state of facial recognition technology prompted the DoD Counterdrug Technology Development Program Office to establish a new set of evaluations. The Facial Recognition Vendor Test 2000 (FRVT 2000) was cosponsored by the DoD Counterdrug Technology Development Program Office, the National Institute of Justice and the Defense Advanced Research Projects Agency and was administered in May and June 2000.

Video-based face recognition using adaptive hidden Markov models

Abstract: While traditional face recognition is typically based on still images, face recognition from video sequences has become popular. In this paper, we propose to use adaptive hidden Markov models (HMM) to perform video-based face recognition. During the training process, the statistics of training video sequences of each subject, and the temporal dynamics, are learned by an HMM. During the recognition process, the temporal characteristics of the test video sequence are analyzed over time by the HMM corresponding to each subject. The likelihood scores provided by the HMMs are compared, and the highest score provides the identity of the test video sequence. Furthermore, with unsupervised learning, each HMM is adapted with the test video sequence, which results in better modeling over time. Based on extensive experiments with various databases, we show that the proposed algorithm results in better performance than using majority voting of image-based recognition results.

The CSU face identification evaluation system: its purpose, features, and structure

Abstract: The CSU Face Identification Evaluation System provides standard face recognition algorithms and standard statistical methods for comparing face recognition algorithms. The system includes standardized image pre-processing software, three distinct face recognition algorithms, analysis software to study algorithm performance, and Unix shell scripts to run standard experiments. All code is written in ANSI C. The preprocessing code replicates feature of preprocessing used in the FERET evaluations. The three algorithms provided are Principle Components Analysis (PCA), a.k.a Eigenfaces, a combined Principle Components Analysis and Linear Discriminant Analysis algorithm (PCA+LDA), and a Bayesian Intrapersonal/Extrapersonal Classifier (BIC). The PCA+LDA and BIC algorithms are based upon algorithms used in the FERET study contributed by the University of Maryland and MIT respectively. There are two analysis. The first takes as input a set of probe images, a set of gallery images, and similarity matrix produced by one of the three algorithms. It generates a Cumulative Match Curve of recognition rate versus recognition rank. The second analysis tool generates a sample probability distribution for recognition rate at recognition rank 1, 2, etc. It takes as input multiple images per subject, and uses Monte Carlo sampling in the space of possible probe and gallery choices. This procedure will, among other things, add standard error bars to a Cumulative Match Curve. The System is available through our website and we hope it will be used by others to rigorously compare novel face identification algorithms to standard algorithms using a common implementation and known comparison techniques.

Face sketch synthesis and recognition

Abstract: We propose a novel face photo retrieval system using sketch drawings. By transforming a photo image into a sketch, we reduce the difference between photo and sketch significantly, thus allow effective matching between the two. To improve the synthesis performance, we separate shape and texture information in a face photo, and conduct transformation on them respectively. Finally a Bayesian classifier is used to recognize the probing sketch from the synthesized pseudo-sketches. Experiments on a data set containing 606 people clearly demonstrate the efficacy of the algorithm.

Expression-invariant 3D face recognition

Abstract: We present a novel 3D face recognition approach based on geometric invariants introduced by Elad and Kimmel. The key idea of the proposed algorithm is a representation of the facial surface, invariant to isometric deformations, such as those resulting from different expressions and postures of the face. The obtained geometric invariants allow mapping 2D facial texture images into special images that incorporate the 3D geometry of the face. These signature images are then decomposed into their principal components. The result is an efficient and accurate face recognition algorithm that is robust to facial expressions. We demonstrate the results of our method and compare it to existing 2D and 3D face recognition algorithms.

Processing faces and facial expressions.

Abstract: This paper reviews processing of facial identity and expressions. The issue of independence of these two systems for these tasks has been addressed from different approaches over the past 25 years. More recently, neuroimaging techniques have provided researchers with new tools to investigate how facial information is processed in the brain. First, findings from "traditional" approaches to identity and expression processing are summarized. The review then covers findings from neuroimaging studies on face perception, recognition, and encoding. Processing of the basic facial expressions is detailed in light of behavioral and neuroimaging data. Whereas data from experimental and neuropsychological studies support the existence of two systems, the neuroimaging literature yields a less clear picture because it shows considerable overlap in activation patterns in response to the different face-processing tasks. Further, activation patterns in response to facial expressions support the notion of involved neural substrates for processing different facial expressions.

A Bayesian discriminating features method for face detection

Abstract: This paper presents a novel Bayesian discriminating features (BDF) method for multiple frontal face detection. The BDF method, which is trained on images from only one database, yet works on test images from diverse sources, displays robust generalization performance. The novelty of this paper comes from the integration of the discriminating feature analysis of the input image, the statistical modeling of face and nonface classes, and the Bayes classifier for multiple frontal face detection. First, feature analysis derives a discriminating feature vector by combining the input image, its 1D Harr wavelet representation, and its amplitude projections. While the Harr wavelets produce an effective representation for object detection, the amplitude projections capture the vertical symmetric distributions and the horizontal characteristics of human face images. Second, statistical modeling estimates the conditional probability density functions, or PDFs, of the face and nonface classes, respectively. While the face class is usually modeled as a multivariate normal distribution, the nonface class is much more difficult to model due to the fact that it includes "the rest of the world." The estimation of such a broad category is, in practice, intractable. However, one can still derive a subset of the nonfaces that lie closest to the face class, and then model this particular subset as a multivariate normal distribution.