Showing papers on "Facial recognition system published in 2002"

Detecting faces in images: a survey

Abstract: Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face, regardless of its 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.

Face detection in color images

Abstract: Human face detection plays an important role in applications such as video surveillance, human computer interface, face recognition, and face image database management. We propose a face detection algorithm for color images in the presence of varying lighting conditions as well as complex backgrounds. Based on a novel lighting compensation technique and a nonlinear color transformation, our method detects skin regions over the entire image and then generates face candidates based on the spatial arrangement of these skin patches. The algorithm constructs eye, mouth, and boundary maps for verifying each face candidate. Experimental results demonstrate successful face detection over a wide range of facial variations in color, position, scale, orientation, 3D pose, and expression in images from several photo collections (both indoors and outdoors).

Face recognition by independent component analysis

Abstract: A number of current face recognition algorithms use face representations found by unsupervised statistical methods. Typically these methods find a set of basis images and represent faces as a linear combination of those images. Principal component analysis (PCA) is a popular example of such methods. The basis images found by PCA depend only on pairwise relationships between pixels in the image database. In a task such as face recognition, in which important information may be contained in the high-order relationships among pixels, it seems reasonable to expect that better basis images may be found by methods sensitive to these high-order statistics. Independent component analysis (ICA), a generalization of PCA, is one such method. We used a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons. ICA was performed on face images in the FERET database under two different architectures, one which treated the images as random variables and the pixels as outcomes, and a second which treated the pixels as random variables and the images as outcomes. The first architecture found spatially local basis images for the faces. The second architecture produced a factorial face code. Both ICA representations were superior to representations based on PCA for recognizing faces across days and changes in expression. A classifier that combined the two ICA representations gave the best performance.

Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition

Abstract: This paper introduces a novel Gabor-Fisher (1936) classifier (GFC) for face recognition. The GFC method, which is robust to changes in illumination and facial expression, applies the enhanced Fisher linear discriminant model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images. The novelty of this paper comes from (1) the derivation of an augmented Gabor feature vector, whose dimensionality is further reduced using the EFM by considering both data compression and recognition (generalization) performance; (2) the development of a Gabor-Fisher classifier for multi-class problems; and (3) extensive performance evaluation studies. In particular, we performed comparative studies of different similarity measures applied to various classifiers. We also performed comparative experimental studies of various face recognition schemes, including our novel GFC method, the Gabor wavelet method, the eigenfaces method, the Fisherfaces method, the EFM method, the combination of Gabor and the eigenfaces method, and the combination of Gabor and the Fisherfaces method. The feasibility of the new GFC method has been successfully tested on face recognition using 600 FERET frontal face images corresponding to 200 subjects, which were acquired under variable illumination and facial expressions. The novel GFC method achieves 100% accuracy on face recognition using only 62 features.

Toward automatic simulation of aging effects on face images

Abstract: The process of aging causes significant alterations in the facial appearance of individuals When compared with other sources of variation in face images, appearance variation due to aging displays some unique characteristics Changes in facial appearance due to aging can even affect discriminatory facial features, resulting in deterioration of the ability of humans and machines to identify aged individuals We describe how the effects of aging on facial appearance can be explained using learned age transformations and present experimental results to show that reasonably accurate estimates of age can be made for unseen images We also show that we can improve our results by taking into account the fact that different individuals age in different ways and by considering the effect of lifestyle Our proposed framework can be used for simulating aging effects on new face images in order to predict how an individual might look like in the future or how he/she used to look in the past The methodology presented has also been used for designing a face recognition system, robust to aging variation In this context, the perceived age of the subjects in the training and test images is normalized before the training and classification procedure so that aging variation is eliminated Experimental results demonstrate that, when age normalization is used, the performance of our face recognition system can be improved

Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class

Abstract: The classical way of attempting to solve the face (or object) recognition problem is by using large and representative data sets. In many applications, though, only one sample per class is available to the system. In this contribution, we describe a probabilistic approach that is able to compensate for imprecisely localized, partially occluded, and expression-variant faces even when only one single training sample per class is available to the system. To solve the localization problem, we find the subspace (within the feature space, e.g., eigenspace) that represents this error for each of the training images. To resolve the occlusion problem, each face is divided into k local regions which are analyzed in isolation. In contrast with other approaches where a simple voting space is used, we present a probabilistic method that analyzes how "good" a local match is. To make the recognition system less sensitive to the differences between the facial expression displayed on the training and the testing images, we weight the results obtained on each local area on the basis of how much of this local area is affected by the expression displayed on the current test image.

Face recognition with radial basis function (RBF) neural networks

Abstract: A general and efficient design approach using a radial basis function (RBF) neural classifier to cope with small training sets of high dimension, which is a problem frequently encountered in face recognition, is presented. In order to avoid overfitting and reduce the computational burden, face features are first extracted by the principal component analysis (PCA) method. Then, the resulting features are further processed by the Fisher's linear discriminant (FLD) technique to acquire lower-dimensional discriminant patterns. A novel paradigm is proposed whereby data information is encapsulated in determining the structure and initial parameters of the RBF neural classifier before learning takes place. A hybrid learning algorithm is used to train the RBF neural networks so that the dimension of the search space is drastically reduced in the gradient paradigm. Simulation results conducted on the ORL database show that the system achieves excellent performance both in terms of error rates of classification and learning efficiency.

Learning gender with support faces

Abstract: Nonlinear support vector machines (SVMs) are investigated for appearance-based gender classification with low-resolution "thumbnail" faces processed from 1,755 images from the FERET (FacE REcognition Technology) face database. The performance of SVMs (3.4% error) is shown to be superior to traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, nearest-neighbor) as well as more modern techniques, such as radial basis function (RBF) classifiers and large ensemble-RBF networks. Furthermore, the difference in classification performance with low-resolution "thumbnails" (21/spl times/12 pixels) and the corresponding higher-resolution images (84/spl times/48 pixels) was found to be only 1%, thus demonstrating robustness and stability with respect to scale and the degree of facial detail.

Face recognition using line edge map

Abstract: The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition.

Discriminant waveletfaces and nearest feature classifiers for face recognition

Abstract: Feature extraction, discriminant analysis, and classification rules are three crucial issues for face recognition. We present hybrid approaches to handle three issues together. For feature extraction, we apply the multiresolution wavelet transform to extract the waveletface. We also perform the linear discriminant analysis on waveletfaces to reinforce discriminant power. During classification, the nearest feature plane (NFP) and nearest feature space (NFS) classifiers are explored for robust decisions in presence of wide facial variations. Their relationships to conventional nearest neighbor and nearest feature line classifiers are demonstrated. In the experiments, the discriminant waveletface incorporated with the NFS classifier achieves the best face recognition performance.

Show Me the Features! Understanding Recognition From the Use of Visual Information

Abstract: We propose an approach that allows a rigorous understanding of the visual categorization and recognition process without asking direct questions about unobservable memory representations. Our approach builds on the selective use of visual information in recognition and a new method (Bubbles) to depict and measure what this information is. We examine three face-recognition tasks (identity, gender, expressive or not) and establish the componential and holistic information responsible for recognition performance. On the basis of this information, we derive task-specific gradients of probability for the allocation of attention to the different regions of the face.

Face identification across different poses and illuminations with a 3D morphable model

Abstract: We present a novel approach for recognizing faces in images taken from different directions and under different illumination. The method is based on a 3D morphable face model that encodes shape and texture in terms of model parameters, and an algorithm that recovers these parameters from a single image of a face. For face identification, we use the shape and texture parameters of the model that are separated from imaging parameters, such as pose and illumination. In addition to the identity, the system provides a measure of confidence. We report experimental results for more than 4000 images from the publicly available CMU-PIE database.

Face Recognition from Long-Term Observations

Abstract: We address the problem of face recognition from a large set of images obtained over time - a task arising in many surveillance and authentication applications. A set or a sequence of images provides information about the variability in the appearance of the face which can be used for more robust recognition. We discuss different approaches to the use of this information, and show that when cast as a statistical hypothesis testing problem, the classification task leads naturally to an information-theoretic algorithm that classifies sets of images using the relative entropy (Kullback-Leibler divergence) between the estimated density of the input set and that of stored collections of images for each class. We demonstrate the performance of the proposed algorithm on two medium-sized data sets of approximately frontal face images, and describe an application of the method as part of a view-independent recognition system.

Principal manifolds and probabilistic subspaces for visual recognition

Abstract: Investigates the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques - principal component analysis (PCA), independent component analysis (ICA) and nonlinear kernel PCA (KPCA) - are examined and tested in a visual recognition experiment using 1,800+ facial images from the "FERET" (FacE REcognition Technology) database. We compare the recognition performance of nearest-neighbor matching with each principal manifold representation to that of a maximum a-posteriori (MAP) matching rule using a Bayesian similarity measure derived from dual probabilistic subspaces. The experimental results demonstrate the simplicity, computational economy and performance superiority of the Bayesian subspace method over principal manifold techniques for visual matching.

Multilinear image analysis for facial recognition

Abstract: Natural images are the composite consequence of multiple factors related to scene structure, illumination, and imaging. For facial images, the factors include different facial geometries, expressions, head poses, and lighting conditions. We apply, multilinear algebra, the algebra of higher-order tensors, to obtain a parsimonious representation of facial image ensembles which separates these factors. Our representation, called TensorFaces, yields improved facial recognition rates relative to standard eigenfaces.

A unified learning framework for real time face detection and classification

Abstract: This paper presents progress toward an integrated, robust, real-time face detection and demographic analysis system. Faces are detected and extracted using the fast algorithm proposed by P. Viola and M.J. Jones (2001). Detected faces are passed to a demographic (gender and ethnicity) classifier which uses the same architecture as the face detector. This demographic classifier is extremely fast, and delivers error rates slightly better than the best-known classifiers. To counter the unconstrained and noisy sensing environment, demographic information is integrated across time for each individual. Therefore, the final demographic classification combines estimates from many facial detections in order to reduce the error rate. The entire system processes 10 frames per second on an 800-MHz Intel Pentium III.

Adjustment Learning and Relevant Component Analysis

Abstract: We propose a new learning approach for image retrieval, which we call adjustment learning, and demonstrate its use for face recognition and color matching. Our approach is motivated by a frequently encountered problem, namely, that variability in the original data representation which is not relevant to the task may interfere with retrieval and make it very difficult. Our key observation is that in real applications of image retrieval, data sometimes comes in small chunks - small subsets of images that come from the same (but unknown) class. This is the case, for example, when a query is presented via a short video clip. We call these groups chunklets, and we call the paradigm which uses chunklets for unsupervised learning adjustment learning. Within this paradigm we propose a linear scheme, which we call Relevant Component Analysis; this scheme uses the information in such chunklets to reduce irrelevant variability in the data while amplifying relevant variability. We provide results using our method on two problems: face recognition (using a database publicly available on the web), and visual surveillance (using our own data). In the latter application chunklets are obtained automatically from the data without the need of supervision.

Evaluation of Gabor-wavelet-based facial action unit recognition in image sequences of increasing complexity

Abstract: Previous work suggests that Gabor-wavelet-based methods can achieve high sensitivity and specificity for emotion-specified expressions (e.g., happy, sad) and single action units (AUs) of the Facial Action Coding System (FACS). This paper evaluates a Gabor-wavelet-based method to recognize AUs in image sequences of increasing complexity. A recognition rate of 83% is obtained for three single AUs when image sequences contain homogeneous subjects and are without observable head motion. The accuracy of AU recognition decreases to 32% when the number of AUs increases to nine and the image sequences consist of AU combinations, head motion, and non-homogeneous subjects. For comparison, an average recognition rate of 87.6% is achieved for the geometry-feature-based method. The best recognition is a rate of 92.7% obtained by combining Gabor wavelets and geometry features.

Face recognition system and method

Abstract: A method of automatically recognizing a human face includes developing a three-dimensional model of a face; and generating a number of two-dimensional images based on the three-dimensional model. The generated two-dimensional images are then enrolled in a database and searched against an input image to identifying the face of the input image.

Facial Identity and Facial Emotions: Speed, Accuracy, and Processing Strategies in Children and Adults

Abstract: As yet, nearly all studies in face and facial affect recognition typically provide only data on the accuracy of processing, invariably also in the absence of reference data on information processing. In this study, accuracy and speed of abstract visuo-spatial processing, face recognition, and facial emotion recognition were investigated in normal school children (7–10 years) and adults (25±4 years). In the age range of 7–10 years, accuracy of facial processing hardly increased, while speed did substantially increase with age. Adults, however, were substantially more accurate and faster than children. Differences between facial and abstract information processing were related to type of processing strategy, that is, configural or holistic processing versus featural or piecemeal processing. Improvement in task performance with age is discussed in terms of an enhanced efficiency of the configural organization of facial knowledge (facial information processing tasks), together with a further increase in proce...

Do I Know You? Processing Orientation and Face Recognition

Abstract: Recognition performance is impaired when people are required to provide a verbal description of a complex stimulus (i.e., verbal-overshadowing effect), such as the face of the perpetrator in a simulated robbery. A shift in the processing operations that support successful face recognition is believed to underlie this effect. Specifically, when participants shift from a global to a local processing orientation, face recognition is impaired. Extending research on this general topic, the present experiment revealed that verbalization is not a necessary precondition for the emergence of impaired recognition performance. Rather, face recognition can be disrupted by a task (i.e., letter identification) that triggers the activation of a local processing orientation. Conversely, the activation of a global processing orientation can enhance the accuracy of face recognition. The theoretical and practical implications of these findings for recent treatments of verbal overshadowing and memory function are considered.

A comparative analysis of face recognition performance with visible and thermal infrared imagery

Abstract: We present a comprehensive performance analysis of multiple appearance-based face recognition methodologies, on visible and thermal infrared imagery. We compare algorithms within and between modalities in terms of recognition performance, false alarm rates and requirements to achieve specified performance levels. The effect of illumination conditions on recognition performance is emphasized, as it underlines the relative advantage of radiometrically calibrated thermal imagery for face recognition.

Contribution of color to face recognition.

Abstract: One of the key challenges in face perception lies in determining how different facial attributes contribute to judgments of identity. In this study, we focus on the role of color cues. Although color appears to be a salient attribute of faces, past research has suggested that it confers little recognition advantage for identifying people. Here we report experimental results suggesting that color cues do play a role in face recognition and their contribution becomes evident when shape cues are degraded. Under such conditions, recognition performance with color images is significantly better than that with gray-scale images. Our experimental results also indicate that the contribution of color may lie not so much in providing diagnostic cues to identity as in aiding low-level image-analysis processes such as segmentation.

Face imaging system for recordal and automated identity confirmation

Abstract: A face imaging system for recordal and/or automated identity confirmation, including a camera unit and a camera unit controller. The camera unit includes a video camera, a rotatable mirror system for directing images of the security area into the video camera, and a ranging unit for detecting the presence of a target and for providing target range data, comprising distance, angle and width information, to the camera unit controller. The camera unit controller includes software for detecting face images of the target, tracking of detected face images, and capture of high quality face images. A communication system is provided for sending the captured face images to an external controller for face verification, face recognition and database searching. Face detection and face tracking is performed using the combination of video images and range data and the captured face images are recorded and/or made available for face recognition and searching.

Non-negative Matrix Factorization for Face Recognition

Abstract: The computer vision problem of face classification under several ambient and unfavorable conditions is considered in this study Changes in expression, different lighting conditions and occlusions are the relevant factors that are studied in this present contribution Non-negative Matrix Factorization (NMF) technique is introduced in the context of face classification and a direct comparison with Principal Component Analysis (PCA) is also analyzed Two leading techniques in face recognition are also considered in this study noticing that NMF is able to improve these techniques when a high dimensional feature space is used Finally, different distance metrics (L1, L2 and correlation) are evaluated in the feature space defined by NMF in order to determine the best one for this specific problem Experiments demonstrate that the correlation is the most suitable metric for this problem