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Abstract: Automatic detection of facial expressions attracts great attention due to its potential applications in human–computer interaction as well as in human facial behavior research. Most of the research has so far been performed in 2D. However, as the limitations of 2D data are understood, expression analysis research is being pursued in 3D face modality. 3D can capture true facial surface data and is less disturbed by illumination and head pose. At this junction we have conducted a comparative evaluation of 3D and 2D face modalities. We have investigated extensively 25 action units (AU) defined in the Facial Action Coding System. For fairness we map facial surface geometry into 2D and apply totally data-driven techniques in order to avoid biases due to design. We have demonstrated that overall 3D data performs better, especially for lower face AUs and that there is room for improvement by fusion of 2D and 3D modalities. Our study involves the determination of the best feature set from 2D and 3D modalities and of the most effective classifier, both from several alternatives. Our detailed analysis puts into evidence the merits and some shortcomings of 3D modality over 2D in classifying facial expressions from single images. [Copyright &y& Elsevier]

Abstract: Automatic analysis of head gestures and facial expressions is a challenging research area and it has significant applications in human-computer interfaces. We develop a face and head gesture detector in video streams. The detector is based on face landmark paradigm in that appearance and configuration information of landmarks are used. First we detect and track accurately facial landmarks using adaptive templates, Kalman predictor and subspace regularization. Then the trajectories (time series) of facial landmark positions during the course of the head gesture or facial expression are converted in various discriminative features. Features can be landmark coordinate time series, facial geometric features or patches on expressive regions of the face. We use comparatively, two feature sequence classifiers, that is, Hidden Markov Models (HMM) and Hidden Conditional Random Fields (HCRF), and various feature subspace classifiers, that is, ICA (Independent Component Analysis) and NMF (Non-negative Matrix Factorization) on the spatiotemporal data. We achieve 87.3% correct gesture classification on a seven-gesture test database, and the performance reaches 98.2% correct detection under a fusion scheme. Promising and competitive results are also achieved on classification of naturally occurring gesture clips of LIlir TwoTalk Corpus. [Copyright &y& Elsevier]

We introduce a similarity learning scheme to improve the 3D object retrieval performance in a relevance feedback setting. The proposed algorithm relies on a score fusion approach that linearly combines elementary similarity scores originating from different shape descriptors into a final similarity function. Each elementary score is modeled in terms of the posterior probability of a database item being relevant to the user-provided query. The posterior parameters are learned via off-line discriminative training, while the optimal combination of weights to generate the final similarity function is obtained by on-line empirical ranking risk minimization. This joint use of on-line and off-line learning methods in relevance feedback not only improves the retrieval performance significantly as compared to the totally unsupervised case, but also outperforms the standard support vector machines based approach. Experiments on several 3D databases, including the Princeton Shape Benchmark, show also that the proposed algorithm has a better small sample behavior. [ABSTRACT FROM AUTHOR]

We address content-based retrieval of complete 3D object models by a probabilistic generative description of local shape properties. The proposed shape description framework characterizes a 3D object with sampled multivariate probability density functions of its local surface features. This density-based descriptor can be efficiently computed via kernel density estimation (KDE) coupled with fast Gauss transform. The nonparametric KDE technique allows reliable characterization of a diverse set of shapes and yields descriptors which remain relatively insensitive to small shape perturbations and mesh resolution. Density-based characterization also induces a permutation property which can be used to guarantee invariance at the shape matching stage. As proven by extensive retrieval experiments on several 3D databases, our framework provides state-of-the-art discrimination over a broad and heterogeneous set of shape categories. [ABSTRACT FROM AUTHOR]

Abstract: We develop a morphosyntax-based natural language watermarking scheme. In this scheme, a text is first transformed into a syntactic tree diagram where the hierarchies and the functional dependencies are made explicit. The watermarking software then operates on the sentences in syntax tree format and executes binary changes under control of Wordnet and Dictionary to avoid semantic drops. A certain level of security is provided via key-controlled randomization of morphosyntactic tools and the insertion of void watermark. The security aspects and payload aspects are evaluated statistically while the imperceptibility is measured using edit-hit counts based on human judgments. It is observed that agglutinative languages are somewhat more amenable to morphosyntax-based natural language watermarking and the free word order property of a language, like Turkish, is an extra bonus. [Copyright &y& Elsevier]

We propose a method to do constrained parameter estimation and inference from neuroimaging data using general linear model (GLM). Constrained approach precludes unrealistic hemodynamic response function (HRF) estimates to appear at the outcome of the GLM analysis. The permissible ranges of waveform parameters were determined from the study of a repertoire of plausible waveforms. These parameter intervals played the role of prior distributions in the subsequent Bayesian analysis of the GLM, and Gibbs sampling was used to derive posterior distributions. The method was applied to artificial null data and near infrared spectroscopy (NIRS) data. The results show that constraining the GLM eliminates unrealistic HRF waveforms and decreases false activations, without affecting the inference for “realistic” activations, which satisfy the constraints. [ABSTRACT FROM AUTHOR]

Abstract: In this paper the contribution of multiresolution analysis to the face recognition performance is examined. We refer to the paradigm that in classification tasks, the use of multiple observations and their judicious fusion at the data, feature or decision level improves the correct decision performance. In our proposed method, prior to the subspace projection operation like principal or independent component analysis, we employ multiresolution analysis to decompose the image into its subbands. Our aim is to search for the subbands that are insensitive to the variations in expression and in illumination. The classification performance is improved by fusing the information coming from the subbands that attain individually high correct recognition rates. The proposed algorithm is tested on face images that differ in expression or illumination separately, obtained from CMU PIE, FERET and Yale databases. Significant performance gains are attained, especially against illumination perturbations. [Copyright &y& Elsevier]

The classification problem of respiratory sound signals has been addressed by taking into account their cyclic nature, and a novel hierarchical decision fusion scheme based on the cooperation of classifiers has been developed. Respiratory signals from three different classes are partitioned into segments, which are later joined to form six different phases of the respiration cycle. Multilayer perceptron classifiers classify the parameterized segments from each phase and decision vectors obtained from different phases are combined using a nonlinear decision combination function to form a final decision on each subject. Furthermore a new regularization scheme is applied to the data to stabilize training and consultation. [Copyright &y& Elsevier]

This paper provides a generalization of non-reducible descriptors by extending the concept of distance between patterns of different classes. Generalized non-reducible descriptors are used in supervised pattern recognition problems where the feature vectors consist of Boolean variables. Generalized non-reducible descriptors are expressed as conjunctions and correspond to syndromes in medical diagnosis. Generalized non-reducible descriptors minimize the number of operations in the decision rules. A mathematical model to construct generalized non-reducible descriptors, a computational procedure, and numerical examples are discussed. [Copyright &y& Elsevier]

The problem of image segmentation using constraint satisfaction neural networks (CSNN) has been considered. Several variations of the CSNN theme have been advanced to improve its performance or to explore new structures. These new segmentation algorithms are based on interplay of additional constraints, of varying the organization of the network or modifying the relaxation scheme. The proposed schemes are tested comparatively on a bank of test images as well as real world images. [Copyright &y& Elsevier]

We address the problem of prototypical waveform extraction in cognitive experiments using functional near-infrared spectroscopy (fNIRS) signals. These waveform responses are evoked with visual stimuli provided in an oddball type experimental protocol. As the statistical signal-processing tool, we consider the linear signal space representation paradigm and use independent component analysis (ICA). The assumptions underlying ICA is discussed in the light of the signal measurement and generation mechanisms in the brain. The ICA-based waveform extraction is validated based both on its conformance to the parametric brain hemodynamic response (BHR) model and to the coherent averaging technique. We assess the intra-subject and inter-subject waveform and parameter variability. [ABSTRACT FROM AUTHOR]

Good representative dictionaries is the most critical part of the BoVW: Bag of Visual Words scheme, used for such tasks as category identification. The paradigm of learning dictionaries from datasets is by far the most widely used approach and there exists a plethora of methods to this effect. Dictionary learning methods demand abundant data, and when the amount of training data is limited, the quality of dictionaries and consequently the performance of BoVW methods suffer. A much less explored path for creating visual dictionaries starts from the knowledge of primitives in appearance models and creates families of parametric shape models. In this work, we develop shape models starting from a small number of primitives and develop a visual dictionary using various nonlinear operations and nonlinear combinations. Compared with the existing model-driven schemes, our method is able to represent and characterize images in various image understanding applications with competitive, and often better performance. [ABSTRACT FROM AUTHOR]

We investigate the source separation problem of random fields within a Bayesian framework. The Bayesian formulation enables the incorporation of prior image models in the estimation of sources. Due to the intractability of the analytical solution, we resort to numerical methods for the joint maximization of the a posteriori distribution of the unknown variables and parameters. We construct the prior densities of pixels using Markov random fields based on a statistical model of the gradient image, and we use a fully Bayesian method with modified-Gibbs sampling. We contrast our work to approximate Bayesian solutions such as Iterated Conditional Modes (ICM) and to non-Bayesian solutions of ICA variety. The performance of the method is tested on synthetic mixtures of texture images and astrophysical images under various noise scenarios. The proposed method is shown to outperform significantly both its approximate Bayesian and non-Bayesian competitors. [ABSTRACT FROM AUTHOR]

The problem of person recognition and verification based on their hand images has been addressed. The system is based on the images of the right hands of the subjects, captured by a flatbed scanner in an unconstrained pose at 45 dpi. In a pre-processing stage of the algorithm, the silhouettes of hand images are registered to a fixed pose, which involves both rotation and translation of the hand and, separately, of the individual fingers. Two feature sets have been comparatively assessed, Hausdorff distance of the hand contours and independent component features of the hand silhouette images. Both the classification and the verification performances are found to be very satisfactory as it was shown that, at least for groups of about five hundred subjects, hand-based recognition is a viable secure access control scheme. [ABSTRACT FROM AUTHOR]

Abstract: The potential of hand shape and hand texture-based biometry is investigated and algorithms are developed. Feature extraction stage is preceded by meticulous registration of the deformable shape of the hand. Alternative features addressing hand shape and hand texture are compared. Independent component analysis features prove to be the best performing in the identification and verification tasks. It is shown that hand biometric devices can be built that perform reliably for a population of at least 1000. [Copyright &y& Elsevier]

We present a compression technique that provides progressive transmission as well as lossless and near-lossless compression in a single framework. The proposed technique produces a bit stream that results in a progressive, and ultimately lossless, reconstruction of an image similar to what one can obtain with a reversible wavelet codec. In addition, the proposed scheme provides near-lossless reconstruction with respect to a given bound, after decoding of each layer of the successively refinable bit stream. We formulate the image data-compression problem as one of successively refining the probability density function (pdf) estimate of each pixel. Within this framework, restricting the region of support of the estimated pdf to a fixed size interval then results in near-lossless reconstruction. We address the context. selection problem, as well as pdf-estimation methods based on context data at any pass. Experimental results for both lossless and near-lossless cases indicate that the proposed compression scheme, that innovatively combines lossless, near-lossless, and progressive coding attributes, gives competitive performance in comparison with state-of-the-art compression schemes. [ABSTRACT FROM AUTHOR]

We present a scalable object tracking framework, which is capable of tracking the contour of nonrigid objects in the presence of occlusion. The framework consists of open-loop boundary prediction and closed-loop boundary correction parts. The open-loop prediction block adaptively divides the object contour into subcontours, and estimates the mapping parameters for each subsegment. The closed-loop boundary correction block employs a suitably weighted combination of low-level features such as color edge, color segmentation, motion models, and motion segmentation for each subcontour. Performance evaluation measures are used in a feedback loop to evaluate the goodness of the segmentation/tracking in order to adjust the weights assigned to each of these low-level features for each subcontour at each frame. The framework is scalable because it can be adapted to track a coarse estimate of the boundary of selected objects in real-time, as well as pixel-accurate boundary tracking in off-line mode. The proposed method does not depend on any single motion or shape model, and does not need training. Experimental results demonstrate that the algorithm is able to track the object boundaries under significant occlusion and background clutter. [ABSTRACT FROM AUTHOR]

We present techniques for steganalysis of images that have been potentially subjected to steganographic algorithms, both within the passive warden and active warden frameworks. Our hypothesis is that steganographic schemes leave statistical evidence that can be exploited for detection with the aid of image quality features and multivariate regression analysis. To this effect image quality metrics have been identified based on the analysis of variance (ANOVA) technique as feature sets to distinguish between cover-images and stego-images. The classifier between cover and stego-images is built using multivariate regression on the selected quality metrics and is trained based on an estimate of the original image. Simulation results with the chosen feature set and wellknown watermarking and steganographic techniques indicate that our approach is able with reasonable accuracy to distinguish between cover and stego images. [ABSTRACT FROM AUTHOR]

In the frequency estimation of sinusoidal signals observed in impulsive noise environments, techniques based on Gaussian noise assumption are unsuccessful. One possible way to find better estimates is to model the noise as an alpha-stable process and to use the fractional lower order statistics (FLOS) of the data to estimate the signal parameters. In this work, we propose a FLOS-based statistical average, the generalized covariation coefficient (GCC). The GCCs of multiple sinusoids for unity moment order in SαS noise attain the same form as the covariance expressions of multiple sinusoids in white Gaussian noise. The subspace-based frequency estimators FLOS–multiple signal classification (MUSIC) and FLOS–Bartlett are applied to the GCC matrix of the data. On the other hand, we show that the multiple sinusoids in SαS noise can also be modeled as a stable autoregressive moving average process approximated by a higher order stable autoregressive (AR) process. Using the GCCs of the data, we obtain FLOS versions of Tufts–Kumaresan (TK) and minimum norm (MN) estimators, which are based on the AR model. The simulation results show that techniques employing lower order statistics are superior to their second-order statistics (SOS)-based counterparts, especially when the noise exhibits a strong impulsive attitude. Among the estimators, FLOS–MUSIC shows a robust performance. It behaves comparably to MUSIC in non-impulsive noise environments, and both in impulsive and non-impulsive high-resolution scenarios. Furthermore, it offers a significant advantage at relatively high levels of impulsive noise contamination for distantly located sinusoidal frequencies. [Copyright &y& Elsevier]

Provides information on the presentation of two design techniques for adaptive orthogonal block transforms based on vector quantization (VQ) codebooks. Information on the selection of reference vectors; Detailed information on the performance results; Conclusions reached.

We propose to model the image differentials of astrophysical source maps by Student's t-distribution and to use them in the Bayesian source separation method as priors. We introduce an efficient Markov Chain Monte Carlo (MCMC) sampling scheme to unmix the astrophysical sources and describe the derivation details. In this scheme, we use the Langevin stochastic equation for transitions, which enables parallel drawing of random samples from the posterior, and reduces the computation time significantly (by two orders of magnitude). In addition, Student's t-distribution parameters are updated throughout the iterations. The results on astrophysical source separation are assessed with two performance criteria defined in the pixel and the frequency domains. [ABSTRACT FROM AUTHOR]

In this paper, we present an extensive study of 3-D face recognition algorithms and examine the benefits of various score-, rank-, and decision-level fusion rules. We investigate face recognizers from two perspectives: the data representation techniques used and the feature extraction algorithms that match best each representation type. We also consider novel applications of various feature extraction techniques such as discrete Fourier transform, discrete cosine transform, nonnegative matrix factorization, and principal curvature directions to the shape modality. We discuss and compare various classifier combination methods such as fixed rules and voting- and rank-based fusion schemes. We also present a dynamic confidence estimation algorithm to boost fusion performance. In identification experiments performed on FRGC v1.0 and FRGC v2.0 face databases, we have tried to find the answers to the following questions: 1) the relative importance of the face representation techniques vis-à-vis the types of features extracted; 2) the impact of the gallery size; 3) the conditions, under which subspace methods are preferable, and the compression factor; 4) the most advantageous fusion level and fusion methods; 5) the role of confidence votes in improving fusion and the style of selecting experts in the fusion; and 6) the consistency of the conclusions across different databases. [ABSTRACT FROM AUTHOR]