Composite support vector machines for detection of faces across views and pose estimation

Support vector machines (SVMs) have shown great potential for learning classification functions that can be applied to object recognition. In this work, we extend SVMs to model the appearance of human faces which undergo non-linear change across multiple views. The approach uses inherent factors in the nature of the input images and the SVM classification algorithm to perform both multi-view face detection and pose estimation.     

Manifold‐Based 3D Face Caricature Generation with Individualized Facial Feature Extraction

Caricature is an interesting art to express exaggerated views of different persons and things through drawing. The face caricature is popular and widely used for different applications. To do this, we have to properly extract unique/specialized features of a person's face. A person's facial feature not only depends on his/her natural appearance, but also the associated expression style. Therefore, we would like to extract the neutural facial features and personal expression style for different applicaions. In this paper, we represent the 3D neutral face models in BU–3DFE database by sparse signal decomposition in the training phase. With this decomposition, the sparse training data can be used for robust linear subspace modeling of public faces. For an input 3D face model, we fit the model and decompose the 3D model geometry into a neutral face and the expression deformation separately. The neutral geomertry can be further decomposed into public face and individualized facial feature. We exaggerate the facial features and the expressions by estimating the probability on the corresponding manifold. The public face, the exaggerated facial features and the exaggerated expression are combined to synthesize a 3D caricature for a 3D face model. The proposed algorithm is automatic and can effectively extract the individualized facial features from an input 3D face model to create 3D face caricature.     

Pose-Robust Facial Expression Recognition Using View-Based 2D $+$ 3D AAM

This paper proposes a pose-robust face tracking and facial expression recognition method using a view-based 2D 3D active appearance model (AAM) that extends the 2D 3D AAM to the view-based approach, where one independent face model is used for a specific view and an appropriate face model is selected for the input face image. Our extension has been conducted in many aspects. First, we use principal component analysis with missing data to construct the 2D 3D AAM due to the missing data in the posed face images. Second, we develop an effective model selection method that directly uses the estimated pose angle from the 2D 3D AAM, which makes face tracking pose-robust and feature extraction for facial expression recognition accurate. Third, we propose a double-layered generalized discriminant analysis (GDA) for facial expression recognition. Experimental results show the following: 1) The face tracking by the view-based 2D 3D AAM, which uses multiple face models with one face model per each view, is more robust to pose change than that by an integrated 2D 3D AAM, which uses an integrated face model for all three views; 2) the double-layered GDA extracts good features for facial expression recognition; and 3) the view-based 2D 3D AAM outperforms other existing models at pose-varying facial expression recognition.     

Matching 2.5D face scans to 3D models

The performance of face recognition systems that use two-dimensional images depends on factors such as lighting and subject's pose. We are developing a face recognition system that utilizes three-dimensional shape information to make the system more robust to arbitrary pose and lighting. For each subject, a 3D face model is constructed by integrating several 2.5D face scans which are captured from different views. 2.5D is a simplified 3D (x,y,z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. Two different modalities provided by the facial scan, namely, shape and texture, are utilized and integrated for face matching. The recognition engine consists of two components, surface matching and appearance-based matching. The surface matching component is based on a modified iterative closest point (ICP) algorithm. The candidate list from the gallery used for appearance matching is dynamically generated based on the output of the surface matching component, which reduces the complexity of the appearance-based matching stage. Three-dimensional models in the gallery are used to synthesize new appearance samples with pose and illumination variations and the synthesized face images are used in discriminant subspace analysis. The weighted sum rule is applied to combine the scores given by the two matching components. Experimental results are given for matching a database of 200 3D face models with 598 2.5D independent test scans acquired under different pose and some lighting and expression changes. These results show the feasibility of the proposed matching scheme.     

无重叠视域的多摄像机之间的目标匹配

在无重叠视域的多摄像机监控中,由于不同摄像机的视域差别和视域分离,同一运动目标在不同的视域中的成像可能会非常不同,因此在这种情况下对运动目标进行匹配是一项具有挑战性的工作。提出了一种可以容忍光照的不同,在无重叠视域的多摄像机下进行目标匹配的方法。该方法经过初始聚类和K-means聚类对目标进行主颜色谱的提取,利用EMKM算法改善K-means对初始中心点的依赖性,把提取出来的主颜色谱直方图作为目标的特征,然后利用特征相似度测量来判定任意两个物体之间是否匹配;当无法对某些物体进行准确匹配时,再利用SIFT特征进行下一步匹配。该方法也可以用于有重叠视域的多摄像机目标匹配中,通过与其他匹配方法相结合,提高匹配的准确度。实验结果证实了该方法具有较高的准确度。     

Video object matching across multiple non-overlapping camera views based on multi-feature fusion and incremental learning

Matching objects across multiple cameras with non-overlapping views is a necessary but difficult task in the wide area video surveillance. Owing to the lack of spatio-temporal information, only the visual information can be used in some scenarios, especially when the cameras are widely separated. This paper proposes a novel framework based on multi-feature fusion and incremental learning to match the objects across disjoint views in the absence of space–time cues. We first develop a competitive major feature histogram fusion representation (CMFH¹) to formulate the appearance model for characterizing the potentially matching objects. The appearances of the objects can change over time and hence the models should be continuously updated. We then adopt an improved incremental general multicategory support vector machine algorithm (IGMSVM²) to update the appearance models online and match the objects based on a classification method. Only a small amount of samples are needed for building an accurate classification model in our method. Several tests are performed on CAVIAR, ISCAPS and VIPeR databases where the objects change significantly due to variations in the viewpoint, illumination and poses. Experimental results demonstrate the advantages of the proposed methodology in terms of computational efficiency, computation storage, and matching accuracy over that of other state-of-the-art classification-based matching approaches. The system developed in this research can be used in real-time video surveillance applications.     

Person re-identification in TV series using robust face recognition and user feedback

In this paper, we present a system for person re-identification in TV series. In the context of video retrieval, person re-identification refers to the task where a user clicks on a person in a video frame and the system then finds other occurrences of the same person in the same or different videos. The main characteristic of this scenario is that no previously collected training data is available, so no person-specific models can be trained in advance. Additionally, the query data is limited to the image that the user clicks on. These conditions pose a great challenge to the re-identification system, which has to find the same person in other shots despite large variations in the person’s appearance. In the study, facial appearance is used as the re-identification cue, since, in contrast to surveillance-oriented re-identification studies, the person can have different clothing in different shots. In order to increase the amount of available face data, the proposed system employs a face tracker that can track faces up to full profile views. This makes it possible to use a profile face image as query image and also to retrieve images with non-frontal poses. It also provides temporal association of the face images in the video, so that instead of using single images for query or target, whole tracks can be used. A fast and robust face recognition algorithm is used to find matching faces. If the match result is highly confident, our system adds the matching face track to the query set. Finally, if the user is not satisfied with the number of returned results, the system can present a small number of candidate face images and lets the user confirm the ones that belong to the queried person. These features help to increase the variation in the query set, making it possible to retrieve results with different poses, illumination conditions, etc. The system is extensively evaluated on two episodes of the TV series Coupling, showing very promising results.     

Probabilistic Models of Appearance for 3-D Object Recognition

We describe how to model the appearance of a 3-D object using multiple views, learn such a model from training images, and use the model for object recognition. The model uses probability distributions to describe the range of possible variation in the object's appearance. These distributions are organized on two levels. Large variations are handled by partitioning training images into clusters corresponding to distinctly different views of the object. Within each cluster, smaller variations are represented by distributions characterizing uncertainty in the presence, position, and measurements of various discrete features of appearance. Many types of features are used, ranging in abstraction from edge segments to perceptual groupings and regions. A matching procedure uses the feature uncertainty information to guide the search for a match between model and image. Hypothesized feature pairings are used to estimate a viewpoint transformation taking account of feature uncertainty. These methods have been implemented in an object recognition system, OLIVER. Experiments show that OLIVER is capable of learning to recognize complex objects in cluttered images, while acquiring models that represent those objects using relatively few views.     

Adaptive multi-cue tracking by online appearance learning

This paper proposes a multi-cue based appearance learning algorithm for object tracking. In each frame, the target object is represented by different cues in the image-as-matrix form. This representation can describe the target from different perspectives and can preserve the spatial correlation information inside the target region. Based on these cues, multiple appearance models are learned online by bilinear subspace analysis to account for the target appearance variations over time. Tracking is formulated within the Bayesian inference framework, in which the observation model is constructed by fusing all the learned appearance models. The combination of online appearance modeling and weight update of each appearance model can adapt our tracking algorithm to both the target and background changes. We test our algorithm on a variety of challenging sequences by tracking car, face, pedestrian, and so on. Experimental results and comparisons to several state-of-the-art methods show improved tracking performance.     

Multi-view face segmentation using fusion of statistical shape and appearance models

This paper demonstrates how a weighted fusion of multiple Active Shape (ASM) or Active Appearance (AAM) models can be utilized to perform multi-view facial segmentation with only a limited number of views available for training the models. The idea is to construct models only from frontal and profile views and subsequently fuse these models with adequate weights to segment any facial view. This reduces the problem of multi-view facial segmentation to that of weight estimation, the algorithm for which is proposed as well. The evaluation is performed on a set of 280 landmarked static face images corresponding to seven different rotation angles and on several video sequences of the AV@CAR database. The evaluation demonstrates that the estimation of the weights does not have to be very accurate in the case of ASM, while in the case of AAM the influence of correct weight estimation is more critical. The segmentation with the proposed weight estimation method produced accurate segmentations in 91% of 280 testing images with the median point-to-point error varying from two to eight pixels (1.8–7.2% of average inter-eye distance).     

Examplar coherent 3D face reconstruction from forensic mugshot database

Reconstructing 3D face models from 2D face images is usually done by using a single reference 3D face model or some gender/ethnicity specific 3D face models. However, different persons, even those of the same gender or ethnicity, usually have significantly different faces in terms of their overall appearance, which forms the base of person recognition via faces. Consequently, existing 3D reference model based methods have limited capability of reconstructing precise 3D face models for a large variety of persons. In this paper, we propose to explore a reservoir of diverse reference models for 3D face reconstruction from forensic mugshot face images, where facial examplars coherent with the input determine the final shape estimation. Specifically, our 3D face reconstruction is formulated as an energy minimization problem with: 1) shading constraint from multiple input face images, 2) distortion and self-occlusion based color consistency between different views, and 3) depth uncertainty based smoothness constraint on adjacent pixels. The proposed energy is minimized in a coarse to fine way, where the shape refinement step is done by using a multi-label segmentation algorithm. Experimental results on challenging datasets demonstrate that the proposed algorithm is capable of recovering high quality 3D face models. We also show that our reconstructed models successfully boost face recognition accuracy.     

Registration and integration of multiple object views for 3D modelconstruction

Automatic 3D object model construction is important in applications ranging from manufacturing to entertainment, since CAD models of existing objects may be either unavailable or unusable. We describe a prototype system for automatically registering and integrating multiple views of objects from range data. The results can then be used to construct geometric models of the objects. New techniques for handling key problems such as robust estimation of transformations relating multiple views and seamless integration of registered data to form an unbroken surface have been proposed and implemented in the system. Experimental results on real surface data acquired using a digital interferometric sensor as well as a laser range scanner demonstrate the good performance of our system     

Estimating face-pose consistency based on synthetic view space

The visual appearance of an object in space is an image configuration projected from a subset of connected faces of the object. It is believed that face perception and face integration play a key role in object recognition in human vision. This paper presents a novel approach for calculating viewpoint consistency for three-dimensional (3D) object recognition, which utilizes the perceptual models of face grouping and face integration. In the approach, faces are used as perceptual entities in accordance with the visual perception of shape constancy and face-pose consistency. To accommodate the perceptual knowledge of face visibility of objects, a synthetic view space (SVS) is developed. SVS is an abstractive perceptual space which partitions and synthesizes the conventional metric view sphere into a synthetic view box in which only a very limited set of synthetic views (s-views) need to be considered in estimating face-pose consistency. The s-views are structurally organized in a network, the view-connectivity net (VCN), which describes all the possible connections and constraints of the s-views in SVS. VCN provides a meaningful mechanism in pruning the search space of SVS during estimating face-pose consistency. The method has been successfully used for recognizing a class of industrial parts     

Predictive models for multibiometric systems

Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations.     

Creating Raster Omnimax Images from Multiple Perspective Views Using the Elliptical Weighted Average Filter

Creation of Omnimax animation by computer opens up fantastic new visual possibilities. Unfortunately, the fish-eye distortion of Omnimax film images complicates synthesis by computer, since most image-synthesis programs can create only perspective views. As an alternative to modifying existing image-synthesis programs to produce Omnimax projections directly, we present a method for creating them from multiple perspective views. Four perspective views of the environment are created, each a projection onto a face of a cube centered at the camera, and then a mapping program creates an Omnimax projection from them. To minimize aliasing during resampling, the mapping program uses the elliptical weighted average filter, a space-variant filter we developed for this application that computes a weighted average over an arbitrarily oriented elliptical area. This filter can also be used for texture mapping 3D surfaces.     

Robust online appearance models for visual tracking

We propose a framework for learning robust, adaptive, appearance models to be used for motion-based tracking of natural objects. The model adapts to slowly changing appearance, and it maintains a natural measure of the stability of the observed image structure during tracking. By identifying stable properties of appearance, we can weight them more heavily for motion estimation, while less stable properties can be proportionately downweighted. The appearance model involves a mixture of stable image structure, learned over long time courses, along with two-frame motion information and an outlier process. An online EM-algorithm is used to adapt the appearance model parameters over time. An implementation of this approach is developed for an appearance model based on the filter responses from a steerable pyramid. This model is used in a motion-based tracking algorithm to provide robustness in the face of image outliers, such as those caused by occlusions, while adapting to natural changes in appearance such as those due to facial expressions or variations in 3D pose.     

Face recognition using multi-class Logical Analysis of Data

This paper addresses the applicability of multi-class Logical Analysis of Data (LAD) as a face recognition technique (FRT). This new classification technique has already been applied in the field of biomedical and mechanical engineering as a diagnostic technique; however, it has never been used in the face recognition literature. We explore how Eigenfaces and Fisherfaces merged to multi-class LAD can be leveraged as a proposed FRT, and how it might be useful compared to other approaches. The aim is to build a single multi-class LAD decision model that recognizes images of the face of different persons. We show that our proposed FRT can effectively deal with multiple changes in the pose and facial expression, which constitute critical challenges in the literature. Comparisons are made both from analytical and practical point of views. The proposed model improves the classification of Eigenfaces and Fisherfaces with minimum error rate.     

Efficient generic face model fitting to images and videos

In this paper we present a robust and lightweight method for the automatic fitting of deformable 3D face models on facial images. Popular fitting techniques such as those based on statistical models of shape and appearance require a training stage based on a set of facial images and their corresponding facial landmarks, which have to be manually labeled. Therefore, new images in which to fit the model cannot differ too much in shape and appearance (including illumination variation, facial hair, wrinkles, etc.) from those used for training. By contrast, our approach can fit a generic face model in two steps: (1) the detection of facial features based on local image gradient analysis and (2) the backprojection of a deformable 3D face model through the optimization of its deformation parameters. The proposed approach can retain the advantages of both learning-free and learning-based approaches. Thus, we can estimate the position, orientation, shape and actions of faces, and initialize user-specific face tracking approaches, such as Online Appearance Models (OAMs), which have shown to be more robust than generic user tracking approaches. Experimental results show that our method outperforms other fitting alternatives under challenging illumination conditions and with a computational cost that allows its implementation in devices with low hardware specifications, such as smartphones and tablets. Our proposed approach lends itself nicely to many frameworks addressing semantic inference in face images and videos.     

An integral approach to free-form object modeling

Presents an approach to free-form object modeling from multiple range images. In most conventional approaches, successive views are registered sequentially. In contrast to the sequential approaches, we propose an integral approach which reconstructs statistically optimal object models by simultaneously aggregating all data from multiple views into a weighted least-squares (WLS) formulation. The integral approach has two components. First, a global resampling algorithm constructs partial representations of the object from individual views, so that correspondence can be established among different views. Second, a weighted least-squares algorithm integrates resampled partial representations of multiple views, using the techniques of principal component analysis with missing data (PCAMD). Experiments show that our approach is robust against noise and mismatch