Showing papers on "Facial recognition system published in 2004"

Two-dimensional PCA: a new approach to appearance-based face representation and recognition

Abstract: In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.

Face Recognition with Local Binary Patterns

Abstract: In this work, we present a novel approach to face recognition which considers both shape and texture information to represent face images. The face area is first divided into small regions from which Local Binary Pattern (LBP) histograms are extracted and concatenated into a single, spatially enhanced feature histogram efficiently representing the face image. The recognition is performed using a nearest neighbour classifier in the computed feature space with Chi square as a dissimilarity measure. Extensive experiments clearly show the superiority of the proposed scheme over all considered methods (PCA, Bayesian Intra/extrapersonal Classifier and Elastic Bunch Graph Matching) on FERET tests which include testing the robustness of the method against different facial expressions, lighting and aging of the subjects. In addition to its efficiency, the simplicity of the proposed method allows for very fast feature extraction.

Convolutional face finder: a neural architecture for fast and robust face detection

Abstract: In this paper, we present a novel face detection approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns, rotated up to /spl plusmn/20 degrees in image plane and turned up to /spl plusmn/60 degrees, in complex real world images. The proposed system automatically synthesizes simple problem-specific feature extractors from a training set of face and nonface patterns, without making any assumptions or using any hand-made design concerning the features to extract or the areas of the face pattern to analyze. The face detection procedure acts like a pipeline of simple convolution and subsampling modules that treat the raw input image as a whole. We therefore show that an efficient face detection system does not require any costly local preprocessing before classification of image areas. The proposed scheme provides very high detection rate with a particularly low level of false positives, demonstrated on difficult test sets, without requiring the use of multiple networks for handling difficult cases. We present extensive experimental results illustrating the efficiency of the proposed approach on difficult test sets and including an in-depth sensitivity analysis with respect to the degrees of variability of the face patterns.

Face detection with the modified census transform

Abstract: Illumination variation is a big problem in object recognition, which usually requires a costly compensation prior to classification. It would be desirable to have an image-to-image transform, which uncovers only the structure of an object for an efficient matching. In this context the contribution of our work is two-fold. First, we introduce illumination invariant local structure features for object detection. For an efficient computation we propose a modified census transform which enhances the original work of Zabih and Woodfill. We show some shortcomings and how to get over them with the modified version. S6econdly, we introduce an efficient four-stage classifier for rapid detection. Each single stage classifier is a linear classifier, which consists of a set of feature lookup-tables. We show that the first stage, which evaluates only 20 features filters out more than 99% of all background positions. Thus, the classifier structure is much simpler than previous described multi-stage approaches, while having similar capabilities. The combination of illumination invariant features together with a simple classifier leads to a real-time system on standard computers (60 msec, image size: 288/spl times/384, 2GHi Pentium). Detection results are presented on two commonly used databases in this field namely the MIT+CMU set of 130 images and the BioID set of 1526 images. We are achieving detection rates of more than 90% with a very low false positive rate of 10/sup -7/%. We also provide a demo program that can be found on the Internet http://www.iis.fraunhofer.de/bv/biometrie/download/.

Dual-space linear discriminant analysis for face recognition

Abstract: Linear discriminant analysis (LDA) is a popular feature extraction technique for face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional face data. Some approaches have been proposed to overcome this problem, but they are often unstable and have to discard some discriminative information. In this paper, a dual-space LDA approach for face recognition is proposed to take full advantage of the discriminative information in the face space. Based on a probabilistic visual model, the eigenvalue spectrum in the null space of within-class scatter matrix is estimated, and discriminant analysis is simultaneously applied in the principal and null subspaces of the within-class scatter matrix. The two sets of discriminative features are then combined for recognition. It outperforms existing LDA approaches.

Facial action recognition for facial expression analysis from static face images

Abstract: Automatic recognition of facial gestures (i.e., facial muscle activity) is rapidly becoming an area of intense interest in the research field of machine vision. In this paper, we present an automated system that we developed to recognize facial gestures in static, frontal- and/or profile-view color face images. A multidetector approach to facial feature localization is utilized to spatially sample the profile contour and the contours of the facial components such as the eyes and the mouth. From the extracted contours of the facial features, we extract ten profile-contour fiducial points and 19 fiducial points of the contours of the facial components. Based on these, 32 individual facial muscle actions (AUs) occurring alone or in combination are recognized using rule-based reasoning. With each scored AU, the utilized algorithm associates a factor denoting the certainty with which the pertinent AU has been scored. A recognition rate of 86% is achieved.

Obtaining person-specific images in a public venue

Abstract: Collecting images of a patron in an entertainment venue is performed by facial recognition of the patron's face within the images, simplifying the storage (37) and distribution of the images (39) for a patron. In order to enhance the reliability of the facial recognition system (31), information about the patron (33) that is not directly related to most facial recognition systems (31), including clothes, height, other associated people, use of glasses and jewelry, disposition of facial hair, and more, can be used. Some of the characteristics used can be specific to a particular date or event, and which will not be more generally characteristic of the patron. The facial recognition system (31) can also be used to identify (37) the patron requesting images to be collected. The present invention also relates to the use of facial recognition in sorting and collecting images from an electronically-stored image collection, enabling the easy retrieval of images that are related to a particular person or set of people.

Fast rotation invariant multi-view face detection based on real Adaboost

Abstract: In this paper, we propose a rotation invariant multi-view face detection method based on Real Adaboost algorithm. Human faces are divided into several categories according to the variant appearance from different viewpoints. For each view category, weak classifiers are configured as confidence-rated look-up-table (LUT) of Haar feature. Real Adaboost algorithm is used to boost these weak classifiers and construct a nesting-structured face detector. To make it rotation invariant, we divide the whole 360-degree range into 12 sub-ranges and construct their corresponding view based detectors separately. To improve performance, a pose estimation method is introduced and results in a processing speed of four frames per second on 320/spl times/240 sized image. Experiments on faces with 360-degree in-plane rotation and /spl mnplus/90-degree out-of-plane rotation are reported, of which the frontal face detector subsystem retrieves 94.5% of the faces with 57 false alarms on the CMU+MlT frontal face test set and the multi-view face detector subsystem retrieves 89.8% of the faces with 221 false alarms on the CMU profile face test set.

A unified framework for subspace face recognition

Abstract: PCA, LDA, and Bayesian analysis are the three most representative subspace face recognition approaches. In this paper, we show that they can be unified under the same framework. We first model face difference with three components: intrinsic difference, transformation difference, and noise. A unified framework is then constructed by using this face difference model and a detailed subspace analysis on the three components. We explain the inherent relationship among different subspace methods and their unique contributions to the extraction of discriminating information from the face difference. Based on the framework, a unified subspace analysis method is developed using PCA, Bayes, and LDA as three steps. A 3D parameter space is constructed using the three subspace dimensions as axes. Searching through this parameter space, we achieve better recognition performance than standard subspace methods.

Face recognition under varying lighting conditions using self quotient image

Abstract: In this paper, we introduce the concept of self-quotient image (SQI) for robust face recognition under varying lighting conditions. It is based on the quotient image method (Amnon Shashua et al., 2001) (T. Riklin-Raviv et al., 1999) to achieve lighting invariance. However, the SQI has three advantages: (1) it needs only one face image for extraction of intrinsic lighting invariant property of a face while removing extrinsic factor corresponding to the lighting; (2) no alignment is needed; (3) it works in shadow regions. The theoretical analysis on conditions where the algorithm is applicable and a non-iterative filtering algorithm for computing SQI are presented. Experiment results demonstrate the effectiveness of our method for robust face recognition under varying lighting conditions.

Names and faces in the news

Abstract: We show quite good face clustering is possible for a dataset of inaccurately and ambiguously labelled face images. Our dataset is 44,773 face images, obtained by applying a face finder to approximately half a million captioned news images. This dataset is more realistic than usual face recognition datasets, because it contains faces captured "in the wild" in a variety of configurations with respect to the camera, taking a variety of expressions, and under illumination of widely varying color. Each face image is associated with a set of names, automatically extracted from the associated caption. Many, but not all such sets contain the correct name. We cluster face images in appropriate discriminant coordinates. We use a clustering procedure to break ambiguities in labelling and identify incorrectly labelled faces. A merging procedure then identifies variants of names that refer to the same individual. The resulting representation can be used to label faces in news images or to organize news pictures by individuals present. An alternative view of our procedure is as a process that cleans up noisy supervised data. We demonstrate how to use entropy measures to evaluate such procedures.

Face detection using improved LBP under Bayesian framework

Abstract: In this paper, we present a novel face detection approach using improved local binary patterns (ILBP) as facial representation. ILBP feature is an improvement of LBP feature that considers both local shape and texture information instead of raw grayscale information and it is robust to illumination variation. We model the face and non-face class using multivariable Gaussian model and classify them under Bayesian framework. Extensive experiments show that the proposed method has an encouraging performance.

Cancelable biometric filters for face recognition

Abstract: In this paper, we address the issue of producing cancelable biometric templates; a necessary feature in the deployment of any biometric authentication system. We propose a novel scheme that encrypts the training images used to synthesize the single minimum average correlation energy filter for biometric authentication. We show theoretically that convolving the training images with any random convolution kernel prior to building the biometric filter does not change the resulting correlation output peak-to-sidelobe ratios, thus preserving the authentication performance. However, different templates can be obtained from the same biometric by varying the convolution kernels thus enabling the cancelability of the templates. We evaluate the proposed method using the illumination subset of the CMU pose, illumination, and expressions (PIE) face dataset. Our proposed method is very interesting from a pattern recognition theory point of view, as we are able to 'encrypt' the data and perform recognition in the encrypted domain that performs as well as the unencrypted case, regardless of the encryption kernel used; we show analytically that the recognition performance remains invariant to the proposed encryption scheme, while retaining the desired shift-invariance property of correlation filters.

A discriminative feature space for detecting and recognizing faces

Abstract: We introduce a novel discriminative feature space which is efficient not only for face detection but also for recognition. The face representation is based on local binary patterns (LBP) and consists of encoding both local and global facial characteristics into a compact feature histogram. The proposed representation is invariant with respect to monotonic gray scale transformations and can be derived in a single scan through the image. Considering the derived feature space, a second-degree polynomial kernel SVM classifier was trained to detect frontal faces in gray scale images. Experimental results using several complex images show that the proposed approach performs favorably compared to the state-of-the-art methods. Additionally, experiments with detecting and recognizing low-resolution faces from video sequences were carried out, demonstrating that the same facial representation can be efficiently used for both detection and recognition.

Appearance-based face recognition and light-fields

Abstract: Arguably the most important decision to be made when developing an object recognition algorithm is selecting the scene measurements or features on which to base the algorithm. In appearance-based object recognition, the features are chosen to be the pixel intensity values in an image of the object. These pixel intensities correspond directly to the radiance of light emitted from the object along certain rays in space. The set of all such radiance values over all possible rays is known as the plenoptic function or light-field. In this paper, we develop a theory of appearance-based object recognition from light-fields. This theory leads directly to an algorithm for face recognition across pose that uses as many images of the face as are available, from one upwards. All of the pixels, whichever image they come from, are treated equally and used to estimate the (eigen) light-field of the object. The eigen light-field is then used as the set of features on which to base recognition, analogously to how the pixel intensities are used in appearance-based face and object recognition.

Boosting local binary pattern (LBP)-Based face recognition

Abstract: This paper presents a novel approach for face recognition by boosting statistical local features based classifiers The face image is scanned with a scalable sub-window from which the Local Binary Pattern (LBP) histograms [14] are obtained to describe the local features of a face image The multi-class problem of face recognition is transformed into a two-class one by classifying every two face images as intra-personal or extra-personal ones [9] The Chi square distance between corresponding Local Binary Pattern histograms of two face images is used as discriminative feature for intra/extra-personal classification We use AdaBoost algorithm to learn a similarity of every face image pairs The proposed method was tested on the FERET FA/FB image sets and yielded an exciting recognition rate of 979%

Component-Based Face recognition with 3D Morphable Models

Abstract: We present a system for pose and illumination invariant face recognition that combines two recent advances in the computer vision field: 3D morphable models and component-based recognition. A 3D morphable model is used to compute 3D face models from three input images of each subject 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 for training a component-based face recognition system. The face recognition module is preceded by a fast hierarchical face detector resulting in a system that can detect and identify faces in video images at about 4 Hz. The system achieved a recognition rate of 88% on a database of 2000 real images of ten people, which is significantly better than a comparable global face recognition system. The results clearly show the potential of the combination of morphable models and component-based recognition towards pose and illumination invariant face recognition.

Face recognition by applying wavelet subband representation and kernel associative memory

Abstract: In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.

Dynamics of Facial Expression Extracted Automatically from Video

Abstract: We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions, including AdaBoost, support vector machines, and linear discriminant analysis. Each video-frame is first scanned in real-time to detect approximately upright-frontal faces. The faces found are scaled into image patches of equal size, convolved with a bank of Gabor energy filters, and then passed to a recognition engine that codes facial expressions into 7 dimensions in real time: neutral, anger, disgust, fear, joy, sadness, surprise. We report results on a series of experiments comparing spatial frequency ranges, feature selection techniques, and recognition engines. Best results were obtained by selecting a subset of Gabor filters using AdaBoost and then training Support Vector Machines on the outputs of the filters selected by AdaBoost. The generalization performance to new subjects for a 7-way forced choice was 93% or more correct on two publicly available datasets, the best performance reported so far on these datasets. Surprisingly, registration of internal facial features was not necessary, even though the face detector does not provide precisely registered images. The outputs of the classifier change smoothly as a function of time and thus can be used for unobtrusive motion capture. We developed an end-to-end system that provides facial expression codes at 24 frames per second and animates a computer generated character. In real-time this expression mirror operates down to resolutions of 16 pixels from eye to eye. We also applied the system to fully automated facial action coding.

Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction

Abstract: Automatic classification is one of the basic tasks required in any pattern recognition and human computer interaction application. In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data. We provide a new analysis that shows under what conditions unlabeled data can be used in learning to improve classification performance. We also show that, if the conditions are violated, using unlabeled data can be detrimental to classification performance. We discuss the implications of this analysis to a specific type of probabilistic classifiers, Bayesian networks, and propose a new structure learning algorithm that can utilize unlabeled data to improve classification. Finally, we show how the resulting algorithms are successfully employed in two applications related to human-computer interaction and pattern recognition: facial expression recognition and face detection.

A face and palmprint recognition approach based on discriminant DCT feature extraction

Abstract: In the field of image processing and recognition, discrete cosine transform (DCT) and linear discrimination are two widely used techniques. Based on them, we present a new face and palmprint recognition approach in this paper. It first uses a two-dimensional separability judgment to select the DCT frequency bands with favorable linear separability. Then from the selected bands, it extracts the linear discriminative features by an improved Fisherface method and performs the classification by the nearest neighbor classifier. We detailedly analyze theoretical advantages of our approach in feature extraction. The experiments on face databases and palmprint database demonstrate that compared to the state-of-the-art linear discrimination methods, our approach obtains better classification performance. It can significantly improve the recognition rates for face and palmprint data and effectively reduce the dimension of feature space.

On a Large Sequence-Based Human Gait Database

Abstract: Biometrics today include recognition by characteristic and by behaviour. Of these, face recognition is the most established with databases having evolved from small single shot single view databases, through multi-shot multi-view and on to current video-sequence databases. Results and potential of a new biometric are revealed primarily by the database on which new techniques are evaluated. Clearly, to ascertain the potential of gait as a biometric, a sequence-based database consisting of many subjects with multiple samples is needed. A large database enables the study of inter-subject variation. Further, issues concerning scene noise (or non-ideal conditions) need to be studied, ideally with a link between ground truth and application based analysis. Thus, we have designed and built a large human gait database, providing a large multi-purpose dataset enabling the investigation of gait as a biometric. In addition, it is also a useful database for many still and sequence based vision applications.

Facial expression recognition using constructive feedforward neural networks

Abstract: A new technique for facial expression recognition is proposed, which uses the two-dimensional (2D) discrete cosine transform (DCT) over the entire face image as a feature detector and a constructive one-hidden-layer feedforward neural network as a facial expression classifier. An input-side pruning technique, proposed previously by the authors, is also incorporated into the constructive learning process to reduce the network size without sacrificing the performance of the resulting network. The proposed technique is applied to a database consisting of images of 60 men, each having five facial expression images (neutral, smile, anger, sadness, and surprise). Images of 40 men are used for network training, and the remaining images of 20 men are used for generalization and testing. Confusion matrices calculated in both network training and generalization for four facial expressions (smile, anger, sadness, and surprise) are used to evaluate the performance of the trained network. It is demonstrated that the best recognition rates are 100% and 93.75% (without rejection), for the training and generalizing images, respectively. Furthermore, the input-side weights of the constructed network are reduced by approximately 30% using our pruning method. In comparison with the fixed structure back propagation-based recognition methods in the literature, the proposed technique constructs one-hidden-layer feedforward neural network with fewer number of hidden units and weights, while simultaneously provide improved generalization and recognition performance capabilities.

Exchanging Faces in Images

Abstract: Pasting somebody’s face into an existing image with traditional photo retouching and digital image processing tools has only been possible if both images show the face from the same viewpoint and with the same illumination. However, this is rarely the case for given pairs of images. We present a system that exchanges faces across large differences in viewpoint and illumination. It is based on an algorithm that estimates 3D shape and texture along with all relevant scene parameters, such as pose and lighting, from single images. Manual interaction is reduced to clicking on a set of about 7 feature points, and marking the hairline in the target image. The system can be used for image processing, virtual try-on of hairstyles, and face recognition. By separating face identity from imaging conditions, our approach provides an abstract representation of images and a novel, high-level tool for image manipulation.

Evaluation of Face Resolution for Expression Analysis

Abstract: Most automatic facial expression analysis (AFEA) systems attempt to recognize facial expressions from data collected in a highly controlled environment with very high resolution frontal faces ( face regions greater than 200 x 200 pixels). However, in real environments, the face image is often in lower resolution and with head motion. It is unclear that the performance of AFEA systems for low resolution face images. The general approach to AFEA consists of 3 steps: face acquisition, facial feature extraction, and facial expression recognition. This paper explores the effects of different image resolutions for each step of facial expression analysis. The different approaches are compared for face detection, face data extraction and expression recognition. A total of five different resolutions of the head region are studied (288x384, 144x192, 72x96, 36x48, and 18Xx24) based on a widely used public database. The lower resolution images are down-sampled from the originals.

Fisher non-negative matrix factorization for learning local features

Abstract: In this paper, we propose a novel subspace method called Fisher non-negative matrix factorization (FNMF) for face recognition. FNMF is based on non-negative matrix factorization (NMF), which is a part-based image representation method proposed by Lee. NMF allows only additive combinations of non-negative basis components. The NMF bases are spatially global, whereas local bases would be preferred. Therefore, Stan et al proposed local non-negative matrix factorization (LNMF) to achieve a localized NMF representation by adding more constraints to enforce spatial locality; one of these constraints enforces the bases to be orthogonal to each other, just like the constraints which PCA imposes on its bases. However, LNMF does not encode discrimination information for a classification problem. In this paper, we impose Fisher constraints on the NMF algorithm, which results in the novel FNMF algorithm. Our experiments show that FNMF achieves better performance than LNMF.

Component-Based Face Recognition with 3D Morphable Models

Abstract: We present a system for pose and illumination invariant face recognition that combines two recent advances in the computer vision field: 3D morphable models and component-based recognition. A 3D morphable model is used to compute 3D face models from three input images of each subject 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 for training a component-based face recognition system. The face recognition module is preceded by a fast hierarchical face detector resulting in a system that can detect and identify faces in video images at about 4 Hz. The system achieved a recognition rate of 88% on a database of 2000 real images of ten people, which is significantly better than a comparable global face recognition system. The results clearly show the potential of the combination of morphable models and component-based recognition towards pose and illumination invariant face recognition.