Shape-appearance-correlated active appearance model

© 2016 Elsevier Ltd Among the challenges faced by current active shape or appearance models, facial-feature localization in the wild, with occlusion in a novel face image, i.e. in a generic environment, is regarded as one of the most difficult computer-vision tasks. In this paper, we propose an Active Appearance Model (AAM) to tackle the problem of generic environment. Firstly, a fast face-model initialization scheme is proposed, based on the idea that the local appearance of feature points can be accurately approximated with locality constraints. Nearest neighbors, which have similar poses and textures to a test face, are retrieved from a training set for constructing the initial face model. To further improve the fitting of the initial model to the test face, an orthogonal CCA (oCCA) is employed to increase the correlation between shape features and appearance features represented by Principal Component Analysis (PCA). With these two contributions, we propose a novel AAM, namely the shape-appearance-correlated AAM (SAC-AAM), and the optimization is solved by using the recently proposed fast simultaneous inverse compositional (Fast-SIC) algorithm. Experiment results demonstrate a 5–10% improvement on controlled and semi-controlled datasets, and with around 10% improvement on wild face datasets in terms of fitting accuracy compared to other state-of-the-art AAM models

Relating Objective and Subjective Performance Measures for AAM-based Visual Speech Synthesizers

We compare two approaches for synthesizing visual speech using Active Appearance Models (AAMs): one that utilizes acoustic features as input, and one that utilizes a phonetic transcription as input. Both synthesizers are trained using the same data and the performance is measured using both objective and subjective testing. We investigate the impact of likely sources of error in the synthesized visual speech by introducing typical errors into real visual speech sequences and subjectively measuring the perceived degradation. When only a small region (e.g. a single syllable) of ground-truth visual speech is incorrect we find that the subjective score for the entire sequence is subjectively lower than sequences generated by our synthesizers. This observation motivates further consideration of an often ignored issue, which is to what extent are subjective measures correlated with objective measures of performance? Significantly, we find that the most commonly used objective measures of performance are not necessarily the best indicator of viewer perception of quality. We empirically evaluate alternatives and show that the cost of a dynamic time warp of synthesized visual speech parameters to the respective ground-truth parameters is a better indicator of subjective quality

Prostate MR image segmentation using 3D active appearance models

This paper presents a method for automatic segmentation of the prostate from transversal T2-weighted images based on 3D Active Appearance Models (AAM). The algorithm consist of two stages. Firstly, Shape Context based non-rigid surface registration of the manual segmented images is used to obtain the point correspondence between the given training cases. Subsequently, an AAM is used to segment the prostate on 50 training cases. The method is evaluated using a 5-fold cross validation over 5 repetitions. The mean Dice similarity coefficient and 95% Hausdorff distance are 0.78 and 7.32 mm respectively

Analysing the importance of different visual feature coefficients

A study is presented to determine the relative importance of different visual features for speech recognition which includes pixel-based, model-based, contour-based and physical features. Analysis to determine the discriminability of features is per- formed through F-ratio and J-measures for both static and tem- poral derivatives, the results of which were found to correlate highly with speech recognition accuracy (r = 0.97). Princi- pal component analysis is then used to combine all visual fea- tures into a single feature vector, of which further analysis is performed on the resulting basis functions. An optimal feature vector is obtained which outperforms the best individual feature (AAM) with 93.5 % word accuracy

Updated Global 3+1 Analysis of Short-BaseLine Neutrino Oscillations

We present the results of an updated fit of short-baseline neutrino oscillation data in the framework of 3+1 active-sterile neutrino mixing. We first consider $\nu_e$ and $\bar\nu_e$ disappearance in the light of the Gallium and reactor anomalies. We discuss the implications of the recent measurement of the reactor $\bar\nu_e$ spectrum in the NEOS experiment, which shifts the allowed regions of the parameter space towards smaller values of $|U_{e4}|^2$. The beta-decay constraints allow us to limit the oscillation length between about 2 cm and 7 m at $3\sigma$ for neutrinos with an energy of 1 MeV. We then consider the global fit of the data in the light of the LSND anomaly, taking into account the constraints from $\nu_e$ and $\nu_\mu$ disappearance experiments, including the recent data of the MINOS and IceCube experiments. The combination of the NEOS constraints on $|U_{e4}|^2$ and the MINOS and IceCube constraints on $|U_{\mu4}|^2$ lead to an unacceptable appearance-disappearance tension which becomes tolerable only in a pragmatic fit which neglects the MiniBooNE low-energy anomaly. The minimization of the global $\chi^2$ in the space of the four mixing parameters $\Delta{m}^2_{41}$, $|U_{e4}|^2$, $|U_{\mu4}|^2$, and $|U_{\tau4}|^2$ leads to three allowed regions with narrow $\Delta{m}^{2}_{41}$ widths at $\Delta m^2_{41} \approx 1.7$ (best-fit), 1.3 (at $2\sigma$), 2.4 (at $3\sigma$) eV$^2$. The restrictions of the allowed regions of the mixing parameters with respect to our previous global fits are mainly due to the NEOS constraints. We present a comparison of the allowed regions of the mixing parameters with the sensitivities of ongoing experiments, which show that it is likely that these experiments will determine in a definitive way if the reactor, Gallium and LSND anomalies are due to active-sterile neutrino oscillations or not.Comment: 39 pages; improved treatment of the reactor flux uncertainties and other minor correction

Ensemble of Hankel Matrices for Face Emotion Recognition

In this paper, a face emotion is considered as the result of the composition of multiple concurrent signals, each corresponding to the movements of a specific facial muscle. These concurrent signals are represented by means of a set of multi-scale appearance features that might be correlated with one or more concurrent signals. The extraction of these appearance features from a sequence of face images yields to a set of time series. This paper proposes to use the dynamics regulating each appearance feature time series to recognize among different face emotions. To this purpose, an ensemble of Hankel matrices corresponding to the extracted time series is used for emotion classification within a framework that combines nearest neighbor and a majority vote schema. Experimental results on a public available dataset shows that the adopted representation is promising and yields state-of-the-art accuracy in emotion classification.Comment: Paper to appear in Proc. of ICIAP 2015. arXiv admin note: text overlap with arXiv:1506.0500

The LSND and MiniBooNE Oscillation Searches at High Δm2\Delta m^2

This paper reviews the results of the LSND and MiniBooNE experiments. The primary goal of each experiment was to effect sensitive searches for neutrino oscillations in the mass region with $\Delta m^2 \sim 1$ eV$^2$. The two experiments are complementary, and so the comparison of results can bring additional information with respect to models with sterile neutrinos. Both experiments obtained evidence for $\bar \nu_\mu \rightarrow \bar \nu_e$ oscillations, and MiniBooNE also observed a $\nu_\mu \rightarrow \nu_e$ excess. In this paper, we review the design, analysis, and results from these experiments. We then consider the results within the global context of sterile neutrino oscillation models. The final data sets require a more extended model than the simple single sterile neutrino model imagined at the time that LSND drew to a close and MiniBooNE began. We show that there are apparent incompatibilities between data sets in models with two sterile neutrinos. However, these incompatibilities may be explained with variations within the systematic error. Overall, models with two (or three) sterile neutrinos seem to succeed in fitting the global data, and they make interesting predictions for future experiments.Comment: Posted with permission from the Annual Review of Nuclear and Particle Science, Volume 63. \c{opyright} 2013 by Annual Reviews, http://www.annualreviews.or

Light Sterile Neutrinos in Particle Physics: Experimental Status

Most of the neutrino oscillation results can be explained by the three-neutrino paradigm. However several anomalies in short baseline oscillation data could be interpreted by invoking a hypothetical fourth neutrino, separated from the three standard neutrinos by a squared mass difference of more than 0.1 eV$^2$. This new neutrino, often called sterile, would not feel standard model interactions but mix with the others. Such a scenario calling for new physics beyond the standard model has to be either ruled out or confirmed with new data. After a brief review of the anomalous oscillation results we discuss the world-wide experimental proposal aiming to clarify the situation.Comment: 14 pages, 2 figures. To appear in the proceedings of the 13th International Conference on Topics in Astroparticle and Underground Physics, TAUP 2013 (F. Avignone & W. Haxton, editors, Physics Procedia, Elsevier) ; Minor revisions in version

3D Face tracking and gaze estimation using a monocular camera

Estimating a user’s gaze direction, one of the main novel user interaction technologies, will eventually be used for numerous applications where current methods are becoming less effective. In this paper, a new method is presented for estimating the gaze direction using Canonical Correlation Analysis (CCA), which finds a linear relationship between two datasets defining the face pose and the corresponding facial appearance changes. Afterwards, iris tracking is performed by blob detection using a 4-connected component labeling algorithm. Finally, a gaze vector is calculated based on gathered eye properties. Results obtained from datasets and real-time input confirm the robustness of this metho