3D face tracking using one monocular camera is an important topic, since it is useful in many domains such as: video surveillance system, human machine interaction, biometrics, etc. In this paper, we propose a new 3D face tracking which is robust to large head rotations. Underlying cascaded regression approach for 2D landmark detection, we build an extension in context of 3D pose tracking. To better work with out-of-plane issues, we extend the training dataset by including a new set of synthetic images. For evaluation, we propose to use a new recording system to capture automatically face pose ground-truth, and create a new test dataset, named U3PT (Unconstrained 3D Pose Tracking). The performance of our method along with the state-of-the-art methods are carried out to analyze advantage as well as limitations need to be improved in the future.

Ultrasonic transient elastography (TE), enables to assess, under active mechanical constraints, the elasticity of the liver, which correlates with hepatic fibrosis stages. This technique is routinely used in clinical practice to assess noninvasively liver stiffness. The Fibroscan system used in this work generates a shear wave via an impulse stress applied on the surface of the skin and records a temporal series of radio-frequency (RF) lines using a single-element ultrasound probe. A shear wave propagation map (SWPM) is generated as a 2-D map of the displacements along depth and time, derived from the correlations of the sequential 1-D RF lines, assuming that the direction of propagation (DOP) of the shear wave coincides with the ultrasound beam axis (UBA). Under the assumption of pure elastic tissue, elasticity is proportional to the shear wave speed. This paper introduces a novel approach to the processing of the SWPM, deriving the maximum likelihood estimate of the shear wave speed when comparing the observed displacements and the estimates provided by the Green's functions. A simple parametric model is used to interface Green's theoretical values of noisy measures provided by the SWPM, taking into account depth-varying attenuation and time-delay. The proposed method was evaluated on numerical simulations using a finite element method simulator and on physical phantoms. Evaluation on this test database reported very high agreements of shear wave speed measures when DOP and UBA coincide.

This paper presents and evaluates a method to localize devices that communicate using a wireless network. The distances that separate a blind node, willing to determine its position, and a set of anchor nodes, that know their locations, are evaluated using the signal attenuation (RSSI) measured on data packets. However, multipath effects, frequent in an indoor scenario, introduce randomness in signal propagation, reducing localization accuracy. We propose to use a maximum likelihood estimator on a two-modes Gaussian Mixture model approach to detect and exclude outlier measurements. We evaluate and compare this method using experimental measurements.

Missing value estimation is important in DNA microarray data analysis. A number of algorithms have been developed to solve this problem, but they have several limitations. Most existing algorithms are not able to deal with the situation where a particular time point (column) of the data is missing entirely. In this paper, we present an autoregressive-model-based missing value estimation method (ARLSimpute) that takes into account the dynamic property of microarray temporal data and the local similarity structures in the data. ARLSimpute is especially effective for the situation where a particular time point contains many missing values or where the entire time point is missing. Experiment results suggest that our proposed algorithm is an accurate missing value estimator in comparison with other imputation methods on simulated as well as real microarray time series datasets.

This paper presents an approach that incorporates Canonical Correlation Analysis (CCA) for monocular 3D face pose and facial animation estimation. The CCA is used to find the dependency between texture residuals and 3D face pose and facial gesture. The texture residuals are obtained from observed raw brightness shape-free 2D image patches that we build by means of a parameterized 3D geometric face model. This method is used to correctly estimate the pose of the face and the model's animation parameters controlling the lip, eyebrow and eye movements (encoded in 15 parameters). Extensive experiments on tracking faces in long real video sequences show the effectiveness of the proposed method and the value of using CCA in the tracking context.

In this paper we consider the localization of a mobile station (MS) in time division multiple access (TDMA) based communication systems. We use joint angle and delay measurements of the emitted signals, impinging on an antenna array at different base stations (BSs). Contrary to previously reported work, our technique takes into account not only the measurement noise, but also multipath propagation and possible BSs reporting only non line of sight (NLOS) measurements. Data are processed using the maximum likelihood (ML) approach based on an implementation of the Expectation Maximization (EM) algorithm. We illustrate the proposed approach using simulated data.

We present in this paper a solution for 3D face and facial feature tracking using canonical correlation analysis and a 3D geometric model. This model is controlled with 17 parameters (6 for the 3D pose, and 11 for facial animation), and is used to crop out reference 2D shape free texture maps from the incoming input frames. Model parameters are updated via image registration in the texture map space. For registration, we use CCA to learn and exploit the dependency between texture residuals and model parameter corrections. We compare tracking results using two kinds of texture maps: one local (image patches around selected vertices of the 3D model), and one global (the whole image patch under the 3D model). Experiments evaluating the effectiveness on the approaches are reported.

We propose a new recursive EM (REM) algorithm that can be used whenever the complete-data model associated to the observed data belongs to an exponential family of distributions. The main characteristic of our approach is to use a stochastic approximation algorithm to approximate the conditional expectation of the complete-data sufficient statistic rather than the unknown parameter itself. Compared to existing approaches, the new algorithm requires no analytical gradient or Hessian computation, it deals with parameter constraints straightforwardly and the resulting estimate can be shown to be Fisher-efficient in general settings. This approach is illustrated on the classic direction of arrival (DOA) model

In this paper, we consider the localization of a source from quantized measurements of time of arrivals (TOA) or time difference of arrivals (TDOA). Applications include, as particular examples, acoustic source localization from a network of microphones under communication constraints, and the localization of a base station using a geolocalized mobile station using tuning advance measurements. We use a maximum likelihood approach, based on an efficient implementation of the EM algorithm. Contrary to previously reported work, our technique takes into account not only the measurement noise, but also the presence of outliers (for example, non line of sight propagation) and the quantization. We illustrate our findings using simulated data and real field measurements

In this paper, an improvement of the quantization optimization algorithm for the MPEG Advanced Audio Coder (AAC) is presented. This algorithm, given a bit-rate constraint, minimizes the perceived distortion generated by the signal compression. The distortion can be related to the quantization error level over frequency subbands through an auditory model. Thus, optimizing the quantization requires knowledge of the rate-distortion function for each subband. When this function can be modeled in a simple way, the algorithm can take a one-loop recursive structure. However, in the MPEG AAC, the rate-distortion function is hard to characterize, since AAC makes use of nonlinear quantizers and variable length entropy coders. As a result, the standard algorithm makes use of two nested loops with a local decoder, in order to measure the error level rather than predicting its value. We first describe a partial subband modeling of the rate-distortion function of interest in the MPEG AAC. Then, using a statistical approach, we find a relationship between the error level and the so-called quantization "scale-factor" and propose a new algorithm that is basically similar to a classical one loop "bit allocation" process. Finally, we describe the complete algorithm and show that it is more efficient than the standard one

The article deals with a technique of voice forgery using the ALISP (automatic language independent speech processing) approach. Such a technique allows the voice of an arbitrary person (the impostor) to be transformed, forging the identity of another person (the client). Our goal is to demonstrate that an automatic speaker recognition system could be seriously threatened by a transformation of this kind. For this purpose, we use a speaker verification system to calculate the likelihood that the forged voice belongs to the genuine client. Experiments on NIST 2004 evaluation data show that the equal error rate for the verification task is significantly increased by our voice transformation.

In an MPEG Advanced Audio Coder (AAC), for each frequency subband, the quantization parameter called scale factor controls the quantization noise and the bit rate. Tuning this coder would require a characterization of the rate-distortion function per subband, which seems to be available only in a parametric manner. In this paper, we show that an accurate model for the quantization noise, depending on the scale factor value, can lead to a simple expression of the relation between the scale factor and the error power per subband. In other terms, we are now able to obtain an explicit writing of the distortion-rate function. In a second time, we apply these results to the coding algorithm in case of a fixed output bit rate and propose a new coding algorithm. Unlike the one described in the informative annex of the MPEG-2 standard, it makes use of a single loop and happens to be faster and more flexible towards psychoacoustic criteria.

A new class of blind equalization cost functions have been proposed (see. Shtrom, V. and Fan, H., IEEE Trans. on SP., vol.46, no.10, p.2674-83, 1998). They have the particularity of being unimodal, and some of them are sectionally convex in the combined system domain (CDS). Both properties seem very attractive for a cost function, though no proof (or specific definition) was given of the sectional convexity. We show, using a particular case of this class, that sectional convexity not only implies fixing a delay, but that it also requires to fix the value of the coefficient associated with such delay. We show that the CMA (constant modulus algorithm) criterion shares the same property, and that this property is difficult to maintain in actual algorithms.

In a MPEG-4 analytic audio scene, spatialization filters can be used after an audio coder. When properly tuned, a MPEG advanced audio coder (AAC), generates an inaudible distortion if a sufficient output bit-rate is available, and, more important in the context of our study, as long as the listener has direct access to the unprocessed output of the decoder. We show that using a spatialization filter after the decoder, as described in the MPEG-4 standard, may damage the perceived quality. We propose solutions and present a modified encoder working with a hearing model that includes all audio processing (including spatialization), which allows bit-rate reduction without quality damage.

The authors deal with frequency-domain (FD) adaptive identification with application to acoustic echo cancellation. Several proposed frequency-domain LMS algorithms are reviewed and unified in the common framework of fast convolution using the short-term Fourier transform. A more flexible identification scheme is then proposed, making it possible to independently control the delay and the adaptation rate. The theoretical proof of convergence of this new scheme is provided together with simulations demonstrating the efficiency of this method in both stationary and nonstationary environments.>

Quantum communication is studied under the cutoff-rate criterion for transmission of M coherent states of real amplitude (M=2, . . ., 7). Comparison with other measurement operators of interest, such as the number and the quasi-classical operators, is made. The effect of quantization of the decision level on the cutoff rate is considered.>

The cutoff rate R/sub 0/ of an optical channel transmitting M-ary phase-shift-keyed coherent states is calculated for a receiver using the optimum quantum detector. This is shown to be nearly equivalent, when M>>1, to a system that measures the phase of the received signal. A closed-form expression yielding a good approximation to R/sub 0/ is derived.>

A new method for bounding the cutoff rateRis presented. As an application, the case of ideal laser input signal with thermal noise on the photon counting PPM channel with soft decision decoding is given. The main result is that the cutoff rate efficiency (defined aslim_{s\rightarrow + \infty} R/s, wheresis the average signal photon number) is given by (1- \gamma)/(1 + \gamma)(where\gamma = n_{0}/(1 + n_{0}), n_{0}being the average thermal noise photons). This value is much smaller than the capacity efficiency,log (1/gamma), given by Pierce et al. in [1].

Calculation of the capacity for a Poisson channel with a source noise as modeled by Pierce is done by bounding the error Probability. Based on Chernoff's bound properties, a more general method yielding a formula for the channel capacity is outlined.

Results of receiver performance for the detection of a coherent light signal in the presence of background radiation are obtained and compared for two different types of background noise: a Gaussian field and a Gaussian field whose complex analytic signal is not circularly symmetric. It is pointed out that the randomness in the noise radiation does not significantly diminish the probability of correct detection.