In the transmit signal path of a cellular terminal that supports the Long-Term Evolution (LTE) standard, the third-order counter-intermodulation (CIM3) product can be a significant contributor to spurious emissions in protected bands, primarily in cases where all the transmit power is concentrated in one resource block at the edge of the allocated bandwidth. This problem can be addressed with a harmonic rejection architecture for the up-conversion mixer in the transceiver. Prior work in this area retains the four-phase baseband inputs of a conventional quadrature mixer, utilizes various techniques in the local oscillator (LO) path involving overlapping LO waveforms to suppress harmonics and requires the digitally-controlled oscillator (DCO) to be operated at two or more times the LO frequency. The harmonic rejection architecture presented in this paper has six baseband phases and six non-overlapping LO phases and, for higher-frequency bands, allows the DCO to run at 1.5 times the LO frequency. It also provides an inherently higher uncalibrated image rejection than architectures with quadrature baseband inputs, which is advantageous for any wireless communication standard that supports higher-order quadrature amplitude modulation (QAM) schemes such as 1024-QAM and 4096-QAM.

Facial age classification is an approach to classify face images into one of several predefined age groups. One of the difficulties in applying learning techniques to the age classification problem is the large amount of labeled training data required. Acquiring such training data is very costly in terms of age progress, privacy, human time, and effort. Although unlabeled face images can be obtained easily, it would be expensive to manually label them on a large scale and getting the ground truth. The frugal selection of the unlabeled data for labeling to quickly reach high classification performance with minimal labeling efforts is a challenging problem. In this paper, we present an active learning approach based on an online incremental bilateral two-dimension linear discriminant analysis (IB2DLDA) which initially learns from a small pool of labeled data and then iteratively selects the most informative samples from the unlabeled set to increasingly improve the classifier. Specifically, we propose a novel data selection criterion called the furthest nearest-neighbor (FNN) that generalizes the margin-based uncertainty to the multiclass case and which is easy to compute, so that the proposed active learning algorithm can handle a large number of classes and large data sizes efficiently. Empirical experiments on FG-NET and Morph databases together with a large unlabeled data set for age categorization problems show that the proposed approach can achieve results comparable or even outperform a conventionally trained active classifier that requires much more labeling effort. Our IB2DLDA-FNN algorithm can achieve similar results much faster than random selection and with fewer samples for age categorization. It also can achieve comparable results with active SVM but is much faster than active SVM in terms of training because kernel methods are not needed. The results on the face recognition database and palmprint/palm vein database showed that our approach can handle problems with large number of classes. Our contributions in this paper are twofold. First, we proposed the IB2DLDA-FNN, the FNN being our novel idea, as a generic on-line or active learning paradigm. Second, we showed that it can be another viable tool for active learning of facial age range classification.

Unimodal analysis of palmprint and palm vein has been investigated for person recognition. However, they are not robust to noise and spoof attacks. In this paper, we present a multimodal personal identification system using palmprint and palm vein images with fusion applied at the image level. The palmprint and palm vein images are fused by a novel integrated line-preserving and contrast-enhancing fusion method. Based on our proposed fusion rule, the modified multiscale edges of palmprint and palm vein images are combined as well as the image contrast and the interaction points (IPs) of the palmprints and vein lines are enhanced. The IPs are novel features obtained in our fused images. A novel palm representation, called "Laplacianpalm" feature, is extracted from the fused images by Locality Preserving Projections (LPP). We compare the recognition performance using the unimodal and the proposed fused images. We also compared the proposed "Laplacianpalm " approach with the Fisherpalm and Eigenpalm on a large dataset. Experimental results show that the proposed multimodal approach provides a better representation and achieves lower error rates in palm recognition.

Optic disk is one of the main components on retina. It is an indicator of various ophthalmic pathologies. This paper presents a novel algorithm to segment the optic disk, based on snake model. The proposed method improves and extends original snake technique in two aspects: clustering and smoothing update. The contour first deforms to the location with the minimal energy, and then self-separates into edge-point group or uncertain-point group by means of weighted k-means algorithm. The contour points are finally updated by variable updating sample numbers. These modifications directly solve the blood vessel problem, which has not been effectively solved in the earlier work on disk boundary detection up to now. The comparative results on the 100 testing images shown that the proposed method achieves better success rate (94%) when compared to those obtained by GVF-snake (12%) and modified ASM method (82%)

In this article, we focus on designing an algorithm, named AdaBoostSVM, using SVM as weak learners for AdaBoost. To obtain a set of effective SVM weak learners, this algorithm adaptively adjusts the kernel parameter in SVM instead of using a fixed one. Compared with the existing AdaBoost methods, the AdaBoostSVM has advantages of easier model selection and better generalization performance. It also provides a possible way to handle the over-fitting problem in AdaBoost. An improved version called Diverse AdaBoostSVM is further developed to deal with the accuracy/diversity dilemma in Boosting methods. By implementing some parameter adjusting strategies, the distributions of accuracy and diversity over these SVM weak learners are tuned to achieve a good balance. To the best of our knowledge, such a mechanism that can conveniently and explicitly balances this dilemma has not been seen in the literature. Experimental results demonstrated that both proposed algorithms achieve better generalization performance than AdaBoost using other kinds of weak learners. Benefiting from the balance between accuracy and diversity, the Diverse AdaBoostSVM achieves the best performance. In addition, the experiments on unbalanced data sets showed that the AdaBoostSVM performed much better than SVM.

Support vector machine has obtained much success in machine learning. But it requires to solve a quadratic optimization (QP) problem so that its training time increases dramatically with the increase of training set. Hence, standard SVM with batch learning has difficulty in handling large scale problems. In this paper, we introduce a SVM accelerator, called sequential bootstrapped SVM (SeqSVM), to speed up the training of SVM. At the beginning, the SeqSVM trains a SVM classifier on a small part of all training samples and then keeps on selecting the so-called convex hull samples from the given large training set to retrain this SVM until all convex hull samples are selected. The key principle in our method is to help the SVM pick the convex hull sample that is wrongly classified by the current SVM and furthest from the current SVM solution. The convex hull sample, which disagrees most with the SVM solution, will lie on the convex hull of each class distribution and all support vectors lie on the convex hull in the case of linearly separable classes. Two difficulties have to be overcome. The first is that the SeqSVM's iterations will take too many if there are too many support vectors. The second is that when the class distributions are not separable, it is not easy to pick convex hull samples. In this paper, we show how these two difficulties are overcome. Experimental results on both artificial database and benchmark databases demonstrated the effectiveness of proposed algorithm to reduce the learning time of SVM on the whole training set.

This paper presents a method that matches interest point features detected on two images taken from different view points. A new multi-scale Plessey corner detector (MPCD) is used to detect the interest points. The geometric constraint between two images is exploited as in [R. Deriche, et al., 1994]: the fundamental matrix is derived using least median of squares (LMedS) from an initial set of matches, and then it is used to guide new matches. However, we propose a new energy function that can approximate affine transformation in a more effective way. Then, the initial set of matches is derived by minimizing the energy function. As a result, our method can perform well even when the pose variation is large between the two images. We compare our method using the proposed MPCD against two standard corner detectors on image matching. Also we evaluate our proposed matching criterion against Zhang's. Our method gave better results in both experiments on real face images.

Image classification is an important task in computer vision. However, how to assign suitable labels to images is a subjective matter, especially when some images can be categorized into multiple classes simultaneously. Multilabel image classification focuses on the problem that each image can have one or multiple labels. It is known that manually labelling images is time-consuming and expensive. In order to reduce the human effort of labelling images, especially multilabel images, we proposed a multilabel SVM active learning method. We also proposed two selection strategies: Max Loss strategy and Mean Max Loss strategy. Experimental results on both artificial data and real-world images demonstrated the advantage of proposed method.

LDA (linear discriminant analysis) and its variants are popular for image-based classification problems such as face recognition. However, their performance is inherently unstable when the samples are sparse. We propose a new type of discriminant analysis called MMDA (margin-maximization discriminant analysis), which derives features by maximizing the average margin between the classes. The method does not require S/sub W/ (within-class scatter matrix) to be non-singular and well-conditioned as it does not involve its inverse term, and the features can be directly derived from the input space. A computational trick has also been proposed for MMDA to handle high-dimensional data. We conducted intensive tests on ORL and UMIST face databases, and the results show that MMDA is a good replacement of LDA for the sparse sample problem.

This paper presents a new physically-based 3D facial model based on anatomical knowledge which provides high fidelity for facial expression animation while optimizing the computation. Our facial model has a multilayer biomechanical structure, incorporating a physically-based approximation to facial skin tissue, a set of anatomically-motivated facial muscle actuators, and underlying skull structure. In contrast to existing mass-spring-damper (MSD) facial models, our dynamic skin model uses the nonlinear springs to directly simulate the nonlinear visco-elastic behavior of soft tissue and a new kind of edge repulsion spring is developed to prevent collapse of the skin model. Different types of muscle models have been developed to simulate distribution of the muscle force applied on the skin due to muscle contraction. The presence of the skull advantageously constrain the skin movements, resulting in more accurate facial deformation and also guides the interactive placement of facial muscles. The governing dynamics are computed using a local semi-implicit ODE solver. In the dynamic simulation, an adaptive refinement automatically adapts the local resolution at which potential inaccuracies are detected depending on local deformation. The method, in effect, ensures the required speedup by concentrating computational time only where needed while ensuring realistic behavior within a predefined error threshold. This mechanism allows more pleasing animation results to be produced at a reduced computational cost.

A new approach for estimating 3D head pose form a monocular image is proposed. Our approach employs general prior knowledge of face structure and the corresponding geometrical constraints provided by the location of vanishing point to determine pose of human faces. Eye-lines and mouth-line are assumed parallel in 3D space, and the vanishing point formed by the intersection of the eyeline and mouth-line in the image can be used to infer 3D orientation and location of human face. Perspective invariance of cross ratio and harmonic range is used to locate the vanishing point stably. The robustness analysis of the algorithm with synthesis data and real face images are enclosed.

We present a novel approach, called the "one-circle " algorithm, for measuring the eye gaze using a monocular image that zooms in on only one eye of a person. Observing that the iris contour is a circle, we estimate the normal direction of this iris circle, considered as the eye gaze, from its elliptical image. From basic projective geometry, an ellipse can be back-projected into space onto two circles of different orientations. However, by using an anthropometric property of the eyeball, the correct solution can be disambiguated. This allows us to obtain a higher resolution image of the iris with a zoom-in camera and thereby achieving higher accuracies in the estimation. The robustness of our gaze determination approach was verified statistically by the extensive experiments on synthetic and real image data. The two key contributions are that we show the possibility of finding the unique eye gaze direction from a single image of one eye and that one can obtain better accuracy as a consequence of this.

Abstract This paper presents new methods for efficient modeling and animation of an hierarchical facial model that conforms to the human face anatomy for realistic and fast 3D facial expression synthesis. The facial model has a skin–muscle–skull structure. The deformable skin model directly simulates the nonlinear visco‐elastic behavior of soft tissue and effectively prevents model collapse. The construction of facial muscles is achieved by using an efficient muscle mapping approach. Based on a cylindrical projection of the texture‐mapped facial surface and wire‐frame skin and skull meshes, this approach ensures different muscles to be located at the anatomically correct positions between the skin and skull layers. For computational efficiency, we devise an adaptive simulation algorithm which uses either a semi‐implicit integration scheme or a quasi‐static solver to compute the relaxation by traversing the designed data structures in a breadth‐first order. The algorithm runs in real‐time and has successfully synthesized realistic facial expressions. ACM CSS:I.3.5 Computer Graphics: Computational Geometry and Object Modelling—physically based modelling;I.3.7 Computer Graphics: Three‐Dimensional Graphics and Realism—animation;

A real-time disparity- and intensity-based head/face tracking system is presented. It integrates stereo, watershed and gradient techniques to track the head/face. The head is tracked if the facial features are not available, e.g. when the person is away from the stereo head or when the head is in the profile view. The face and 3D pose are tracked once the facial features, e.g. eyebrows, eyes, mouth and nostrils, are available. A histogram-based disparity segmentation method is developed to locate the person of interest. A novel watershed-based method is proposed in which the head can be separated from the connected head-and shoulder component. The facial features are extracted within the facial region using a new method called "anti max-median filter". The face contour is refined using the features and the gradient model developed in, however the head in our approach is allowed to be in arbitrary direction. A commercially available stereo system was used to implement the head/face tracking. Our approach automatically initializes without user intervention and can be re-initialized automatically if tracking of the 3D face pose is lost. The results on our face database and real image video sequences illustrate the effectiveness of the method.

Iris Technology has been successfully applied to person verification and identification. However, all commercial products require user cooperation for iris image capture. This paper examines the new challenges of iris recognition when extended to less cooperative situations. With the current stress on security and surveillance, this has been an important consideration. First, a summary of research findings of the past decade on iris recognition is described Then we identified new challenges that will be encountered when extending these methods to less cooperative situations. The difficulties are great and this paper describes some initial work into this area. One difficulty studied is the loss of iris details captured. We propose a modified Kolmogorov complexity measure based on maximum Shannon entropy of wavelet packet reconstruction to quantify the iris information. Real-time eye-corner tracking, iris segmentation and feature extraction algorithms are implemented. Video images of the iris are captured by an ordinary CCD camera with a zoom lens. Experiments are performed and the performances and analysis of iris code method and correlation method are described. Several useful findings were reached albeit from a small database. The iris codes were found to contain almost all the discriminating information. Our correlation approach coupled with nearest neighbour classification outperforms the conventional thresholding method for iris recognition with degraded images.

In this paper we propose an individualized 3D facial model constructed from the anatomical perspective for realistic facial expression synthesis. Our model incorporates a physically-based approximation to facial skin tissue, a set of anatomically-motivated facial muscle actuators and underlying skull structure. The skin is modeled as a multi-layer mass-spring lattice by taking into account the nonlinear stress-strain relationship of the real skin. The 3D face model incorporates a skull structure which extends the scope of facial motion and facilitates facial muscle definition. The automatic facial muscle construction is achieved by using an efficient muscle mapping approach that ensures different muscles to be located at the anatomically correct positions between the skin and skull layers. For computational efficiency, we devise a semi-implicit integration method to numerically solve the governing dynamics. The dynamic facial animation algorithm runs at an interactive rate with realistic facial expressions to be synthesized.

We present a system for creating a photorealistic 3D face model of a specific person with a hierarchical structure for dynamic facial expression animation. The facial modeling procedure starts from the individual facial measurements which provides a highly accurate facial geometry and color information. Based on the reconstructed facial mesh, a deformable multi-layer soft tissue model is developed to simulate the dynamic behavior of the skin by taking into account its nonlinear stress-strain relationship. The underlying muscle and skull structure are physically modeled and integrated with the skin meshes. By taking as input a collection of reflectance images covering the face, the view-based texture blending method automatically generates a comprehensive texture map for photorealistic rendering. The resulting personalized face model with biomechanical structure is animated by numerically solving the governing dynamic equation. Using our system, we have synthesized realistic expressions on the authentic face model of a specific individual.

There are two components to the human visual line-of-sight: pose of human head and the orientation of the eye within their sockets. We have investigated these two aspects but will concentrate on eye gaze estimation. We present a novel approach called the "one-circle" algorithm for measuring the eye gaze using a monocular image that zooms in on only one eye of a person. Observing that the iris contour is a circle, we estimate the normal direction of this iris circle, considered as the eye gaze, from its elliptical image. From basic projective geometry, an ellipse can be back-projected into space onto two circles of different orientations. However, by using a geometric constraint, namely, that the distance between the eyeball's center and the two eye corners should be equal to each other, the correct solution can be disambiguated. This allows us to obtain a higher resolution image of the iris with a zoom-in camera, thereby achieving higher accuracies in the estimation. A general approach that combines head pose determination with eye gaze estimation is also proposed. The searching of the eye gaze is guided by the head pose information. The robustness of our gaze determination approach was verified statistically by the extensive experiments on synthetic and real image data. The two key contributions are that we show the possibility of finding the unique eye gaze direction from a single image of one eye and that one can obtain better accuracy as a consequence of this.

The paper presents a physically-based 3D facial model based on anatomical knowledge for facial expression animation. The facial model incorporates a physically-based approximation to facial skin and a set of anatomically-motivated facial muscles. The skin model is established through the use of a mass-spring system with nonlinear springs which simulate the elastic-dynamics of a real facial skin. Muscle models are developed to emulate facial muscle contraction. Lagrangian mechanics governs the dynamics, dictating the deformation of facial surface in response to muscular forces. We show that when surface regions are influenced by the large muscular force, the local deformation becomes inaccurate. The conventional method to deal with this problem is using a fine network, but it also increases the cost of computation. We therefore present an approach to adaptively refine the mass-spring facial model to a required accuracy. It generates more pleasing results at low computational expense.

We propose a physically-based approach based on anatomical knowledge for real-time facial expression animation. The facial model incorporates a physically-based approximation to facial skin and a set of anatomically-motivated facial muscles. The skin is modeled by a mass-spring system with nonlinear springs which have a biphasic stress-strain relationship to simulate the elastic dynamics of real facial skin. Facial muscles are modeled as forces deforming the spring mesh. Based on the action units (AUs) of the facial action coding system (FACS), various expressions can be generated by a combination of contractions of a set of facial muscles. Lagrangian mechanics governs the dynamics, dictating the deformation of the facial surface in response to muscle forces. Simulation results show real-time facial deformation as well as realistic expression animation.

This paper presents a space-time adaptive refinement approach to simulate facial expressions on the deformable 3D face model at interactive rate. This new computational model benefits from an adaptive sampling of both space and time to minimize computational cost. Using this technique, we achieve a guaranteed frame rate of the dynamic facial expression simulation at low computational expense.

Abstract In this paper, we present a new approach for measuring eye-gaze of human via images of the irises. Two iris contours are approximately modeled as two circles having known radii and we estimate the ellipses of their projections onto image plane, respectively. The eye-gaze, defined in this paper as the unit surface normal to the supporting plane of the iris, can be estimated from the projection of the iris contour (limbus). The unique solution of the eye-gaze was obtained based on a geometric constraint, namely that the difference between the two normal directions to the supporting planes of the left and right iris should be minimal, irrespective of eyeball rotations and head movements. Furthermore, the 3D location of the center of the irises can be calculated if the radius of the iris contour is given. The robustness of this approach was verified by experiments on synthetic and real image data. In order to obtain a higher resolution of the iris, a zoom-in camera is used. This creates an additional problem of having to relate the camera for estimating the head-pose to the iris camera. A general approach that combines head-pose determination with gaze estimation is proposed.

A new approach for detecting front-view facial contour is described. A novel contribution is to eliminate the ears and neck from the segmented face region by using morphological operations and knowledge about the face structure. Some consistent rules are derived from studying a large number of cases to locate some contour points from the skeleton of the skin-color segmented frontal face region. We extend our previous work (J.G. Wang and E. Sung, 1999), to solving face contour detection of tilted faces as well. The face contour can then be fitted as an ellipse using these points. The least inertia axis of the facial region is used as symmetry axis of the facial region that guarantees the facial contour can also be detected from a tilted face image. Experiments have been done with a number of images of a frontal view of the head including some with a slight pan obtained from an Internet face database. Also, a number of face images captured by a color CCD camera are tried using our method. The robustness is confirmed with a correct detection rate of over 94%.

For signature verification, there can be a large number of features available in a signature. However, not all these features are of use as some even could be unfavorable for verification of particular signatures. Finding an optimal personalized subset of all the possible features for a signer is crucial for signature verification systems, which are based on a parameter approach. This paper proposes a novel automated optimal personalized feature selection method based on genetic algorithms. Feature vectors for a specific signer are encoded into a population of genes or chromosomes. Through a process of genetic evolution with the application of specific genetic crossover and mutation operations an optimized personalized feature vector is obtained. An important characteristic of the method is that the number and type of features obtained for each signer through genetic evolution is not fixed and predetermined. Experimental results are presented to demonstrate the effectiveness of this novel approach to automated signature verification.