Search

Inspired by Weber's Law, this paper proposes a simple, yet very powerful and robust local descriptor, called the Weber Local Descriptor (WLD). It is based on the fact that human perception of a pattern depends not only on the change of a stimulus (such as sound, lighting) but also on the original intensity of the stimulus. Specifically, WLD consists of two components: differential excitation and orientation. The differential excitation component is a function of the ratio between two terms: One is the relative intensity differences of a current pixel against its neighbors, the other is the intensity of the current pixel. The orientation component is the gradient orientation of the current pixel. For a given image, we use the two components to construct a concatenated WLD histogram. Experimental results on the Brodatz and KTH-TIPS2-a texture databases show that WLD impressively outperforms the other widely used descriptors (e.g., Gabor and SIFT). In addition, experimental results on human face detection also show a promising performance comparable to the best known results on the MIT+CMU frontal face test set, the AR face data set, and the CMU profile test set. [ABSTRACT FROM AUTHOR]

Gabor features have been known to be effective for face recognition. However, only a few approaches utilize phase feature and they usually perform worse than those using magnitude feature. To investigate the potential of Gabor phase and its fusion with magnitude for face recognition, in this paper, we first propose local Gabor XOR patterns (LGXP), which encodes the Gabor phase by using the local XOR pattern (LXP) operator. Then, we introduce block-based Fisher's linear discriminant (BFLD) to reduce the dimensionality of the proposed descriptor and at the same time enhance its discriminative power. Finally, by using BFLD, we fuse local patterns of Gabor magnitude and phase for face recognition. We evaluate our approach on FERET and FRGC 2.0 databases. In particular, we perform comparative experimental studies of different local Gabor patterns. We also make a detailed comparison of their combinations with BFLD, as well as the fusion of different descriptors by using BFLD. Extensive experimental results verify the effectiveness of our LGXP descriptor and also show that our fusion approach outperforms most of the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which exploits both global and local discriminative features. In this method, global features are extracted from the whole face images by keeping the low-frequency coefficients of Fourier transform, which we believe encodes the holistic facial information, such as facial contour. For local feature extraction, Gabor wavelets are exploited considering their biological relevance. After that, Fisher's linear discriminant (FLD) is separately applied to the global Fourier features and each local patch of Gabor features. Thus, multiple FLD classifiers are obtained, each embodying different facial evidences for face recognition. Finally, all these classifiers are combined to form hierarchical ensemble classifier. We evaluate the proposed method using two large-scale face databases: FERET and FRGC version 2.0. Experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol. [ABSTRACT FROM AUTHOR]

No abstract received. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method to estimate the head yaw rotations based on the asymmetry of 2-D facial appearance. In traditional appearance-based pose estimation methods, features are typically extracted holistically by subspace analysis such as principal component analysis, linear discriminant analysis (LDA), etc., which are not designed to directly model the pose variations. In this paper, we argue and reveal that the asymmetry in the intensities of each row of the face image is closely relevant to the yaw rotation of the head and, at the same time, evidently insensitive to the identity of the input face. Specifically, to extract the asymmetry information, 1-D Gabor filters and Fourier transform are exploited. LDA is further applied to the asymmetry features to enhance the discrimination ability. By using the simple nearest centroid classifier, experimental results on two multipose databases show that the proposed features outperform other features. In particular, the generalization of the proposed asymmetry features is verified by the impressive performance when the training and the testing data sets are heterogeneous. [ABSTRACT FROM AUTHOR]

The variation of facial appearance due to the viewpoint (/pose) degrades face recognition systems considerably, which is one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given nonfrontal view to obtain a virtual gallery/probe face. Following this idea, this paper proposes a simple, but efficient, novel locally linear regression (LLR) method, which generates the virtual frontal view from a given nonfrontal face image. We first justify the basic assumption of the paper that there exists an approximate linear mapping between a nonfrontal face image and its frontal counterpart. Then, by formulating the estimation of the linear mapping as a prediction problem, we present the regression-based solution, i.e., globally linear regression. To improve the prediction accuracy in the case of coarse alignment, LLR is further proposed. In LLR, we first perform dense sampling in the nonfrontal face image to obtain many overlapped local patches. Then, the linear regression technique is applied to each small patch for the prediction of its virtual frontal patch. Through the combination of all these patches, the virtual frontal view is generated. The experimental results on the CMU PIE database show distinct advantage of the proposed method over Eigen light-field method. [ABSTRACT FROM AUTHOR]

A novel object descriptor, histogram of Gabor phase pattern (HGPP), is proposed for robust face recognition. In HGPP, the quadrant-bit codes are first extracted from faces based on the Gabor transformation. Global Gabor phase pattern (GGPP) and local Gabor phase pattern (LGPP) are then proposed to encode the phase variations. GGPP captures the variations derived from the orientation changing of Gabor wavelet at a given scale (frequency), while LGPP encodes the local neighborhood variations by using a novel local XOR pattern (LXP) operator. They are both divided into the nonoverlapping rectangular regions, from which spatial histograms are extracted and concatenated into an extended histogram feature to represent the original image. Finally, the recognition is performed by using the nearest-neighbor classifier with histogram intersection as the similarity measurement. The features of HGPP lie in two aspects: 1) HGPP can describe the general face images robustly without the training procedure; 2) HGPP encodes the Gabor phase information, while most previous face recognition methods exploit the Gabor magnitude information. In addition, Fisher separation criterion is further used to improve the performance of HGPP by weighing the subregions of the image according to their discriminative powers. The proposed methods are success fully applied to face recognition, and the experiment results on the large-scale FERET and CAS-PEAL databases show that the proposed algorithms significantly outperform other well-known systems in terms of recognition rate. [ABSTRACT FROM AUTHOR]

In this article, we present an individual appearance model based method, named face-specific subspace (FSS), for recognizing human faces under variation in lighting, expression, and viewpoint. This method derives from the traditional Eigenface but differs from it in essence. In Eigenface, each face image is represented as a point in a low-dimensional face subspace shared by all faces; however, the experiments conducted show one of the demerits of such a strategy: it fails to accurately represent the most discriminanting features of a specific face. Therefore, we propose to model each face with one individual face subspace, named Face-Specific Subspace. Distance from the face-specific subspace, that is, the reconstruction error, is then exploited as the similarity measurement for identification. Furthermore, to enable the proposed approach to solve the single example problem, a technique to derive multisamples from one single example is further developed. Extensive experiments on several academic databases show that our method significantly outperforms Eigenface and template matching, which intensively indicates its robustness under variation in illumination, expression, and viewpoint. © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol 13: 23–32, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10047 [ABSTRACT FROM AUTHOR]

In this paper, we present a compositional and dynamic model for face aging. The compositional model represents faces in each age group by a hierarchical And-Or graph, in which And nodes decompose a face into parts to describe details (e.g., hair, wrinkles, etc.) crucial for age perception and Or nodes represent large diversity of faces by alternative selections. Then a face instance is a transverse of the And-Or graph--parse graph. Face aging is modeled as a Markov process on the parse graph representation. We learn the parameters of the dynamic model from a large annotated face data set and the stochasticity of face aging is modeled in the dynamics explicitly. Based on this model, we propose a face aging simulation and prediction algorithm. Inversely, an automatic age estimation algorithm is also developed under this representation. We study two criteria to evaluate the aging results using human perception experiments: 1) the accuracy of simulation: whether the aged faces are perceived of the intended age group, and 2) preservation of identity: whether the aged faces are perceived as the same person. Quantitative statistical analysis validates the performance of our aging model and age estimation algorithm. [ABSTRACT FROM AUTHOR]

It is well known that a new face studied from one view is often difficult to identify from another. This viewpoint dependence has detrimental implications for forensic practice. To compensate for this problem, we employed synthesized face images in the training session of a standard old/new recognition task. Observers in the experimental conditions learned one or more synthesized face images along with an original photograph of the face in a different view, whereas observers in the control conditions learned only the original photograph of the face. It was found that the experimental conditions consistently produced better recognition accuracy than the control conditions. We conclude that synthesized face views can be used to facilitate person identification in forensic applications. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we describe the acquisition and contents of a large-scale Chinese face database: the CAS-PEAL face database. The goals of creating the CAS-PEAL face database include the following: 1) providing the worldwide researchers of face recognition with different sources of variations, particularly pose, expression, accessories, and lighting (PEAL), and exhaustive ground-truth information in one uniform database; 2) advancing the state-of-the-art face recognition technologies aiming at practical applications by using off-the-shelf imaging equipment and by designing normal face variations in the database; and 3) providing a large-scale face database of Mongolian. Currently, the CAS-PEAL face database contains 99594 images of 1040 individuals (595 males and 445 females). A total of nine cameras are mounted horizontally on an arc arm to simultaneously capture images across different poses. Each subject is asked to look straight ahead, up, and down to obtain 27 images in three shots. Five facial expressions, six accessories, and 15 lighting changes are also included in the database. A selected subset of the database (CAS-PEAL-R1, containing 30863 images of the 1040 subjects) is available to other researchers now. We discuss the evaluation protocol based on the CAS-PEAL-R1 database and present the performance of four algorithms as a baseline to do the following: 1) elementarily assess the difficulty of the database for face recognition algorithms; 2) preference evaluation results for researchers using the database; and 3) identify the strengths and weaknesses of the commonly used algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method to estimate the head yaw rotation using the symmetry of regions. We argue that the symmetry of 2D regions located in the same horizontal row is more intrinsically relevant to the yaw rotation of head than the symmetry of 1D signals, while at the same time insensitive to the identity of the face. Specifically, the proposed method relies on the effective combination of Gabor filters and covariance descriptors. We first extract the multi-scale and multi-orientation Gabor representations of the input face image, and then use covariance descriptors to compute the symmetry between two regions in terms of Gabor representations under the same scale and orientation. Since the covariance matrix can alleviate the influence caused by rotations and illumination, the proposed method is robust to such variations. In addition, the proposed method is further improved by combining it with a metric learning method named aa KISS MEtric learning (KISSME). Experiments on four challenging databases demonstrated that the proposed method outperformed the state of the art. [ABSTRACT FROM AUTHOR]

As a large-scale database of hundreds of thousands of face images collected from the Internet and digital cameras becomes available, how to utilize it to train a well-performed face detector is a quite challenging problem. In this paper, we propose a method to resample a representative training set from a collected large-scale database to train a robust human face detector. First, in a high-dimensional space, we estimate geodesic distances between pairs of face samples/examples inside the collected face set by isometric feature mapping (Isomap) and then subsample the face set. After that, we embed the face set to a low-dimensional manifold space and obtain the low-dimensional embedding. Subsequently, in the embedding, we interweave the face set based on the weights computed by locally linear embedding (LLE). Furthermore, we resample nonfaces by Isomap and LLE likewise. Using the resulting face and nonface samples, we train an AdaBoost-based face detector and run it on a large database to collect false alarms. We then use the false detections to train a one-class support vector machine (SVM). Combining the AdaBoost and one-class SVM-based face detector, we obtain a stronger detector. The experimental results on the MIT + CMU frontal face test set demonstrated that the proposed method significantly outperforms the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a new system that applies an example-based learning method to learn facial motion patterns from a video sequence of individual facial behavior such as lip motion and facial expression, and using that to create vivid three dimensional (3-D) face animation according to the definition of MPEG-4 face animation parameters. The system consists of three key modules, face tracking, pattern learning, and face animation. In face tracking, to reduce the complexity of the tracking process, a novel coarse-to-fine strategy combined with a Kalman filter is proposed for localizing key facial landmarks in each image of the video. The landmarks' sequence is normalized into a visual feature matrix and then fed to the next step of process. In pattern learning, in the pretraining stage, the parameters of the camera that took the video are requested with the training video data so the system can estimate the basic mapping from a normalized two-dimensional (2-D) visual feature matrix to the representation in 3-D MPEG-4 face animation parameter space, in assistance with the computer vision method. In the practice stage, considering that in most cases camera parameters are not provided with video data, the system uses machine learning technology to complement the incomplete 3-D information for the mapping that information is needed in face orientation presentation. The example-based learning in this system integrates several methods including clustering, HMM, and ANN to make a better conversion from a 2-D to 3-D model and better estimation of incomplete 3-D information for good mapping; this will be used to drive face animation thereafter. In face animation, the system can synthesize face animation following any type of face motion in video. Experiments show that our system produces more vivid face motion animation, compared to other early systems. [ABSTRACT FROM AUTHOR]