Learning based automatic face annotation for arbitrary poses and expressions from frontal images only

Statistical approaches for building non-rigid deformable models, such as the active appearance model (AAM), have enjoyed great popularity in recent years, but typically require tedious manual annotation of training images. In this paper, a learning based approach for the automatic annotation of visually deformable objects from a single annotated frontal image is presented and demonstrated on the example of automatically annotating face images that can be used for building AAMs for fitting and tracking. This approach employs the idea of initially learning the correspondences between landmarks in a frontal image and a set of training images with a face in arbitrary poses. Using this learner, virtual images of unseen faces at any arbitrary pose for which the learner was trained can be reconstructed by predicting the new landmark locations and warping the texture from the frontal image. View-based AAMs are then built from the virtual images and used for automatically annotating unseen images, including images of different facial expressions, at any random pose within the maximum range spanned by the virtually reconstructed images. The approach is experimentally validated by automatically annotating face images from three different databases

Investigating LSTM for micro-expression recognition

This study investigates the utility of Long Short-Term Memory (LSTM) networks for modelling spatial-temporal patterns for micro-expression recognition (MER). Micro-expressions are involuntary, short facial expressions, often of low intensity. RNNs have attracted a lot of attention in recent years for modelling temporal sequences. The RNN-LSTM combination to be highly effective results in many application areas. The proposed method combines the recent VGGFace2 model, basically a ResNet-50 CNN trained on the VGGFace2 dataset, with uni-directional and bi-directional LSTM to explore different ways modelling spatial-temporal facial patterns for MER. The Grad-CAM heat map visualisation is used in the training stages to determine the most appropriate layer of the VGGFace2 model for retraining. Experiments are conducted with pure VGGFace2, VGGFace2 + uni-directional LSTM, and VGGFace2 + Bi-directional LSTM on the SMIC database using 5-fold cross-validation

Examining the Influence of Personality and Multimodal Behavior on Hireability Impressions

While personality traits have been traditionally modeled as behavioral constructs, we novelly posit job hireability as a personality construct. To this end, we examine correlates among personality and hireability measures on the First Impressions Candidate Screening dataset. Modeling hireability as both a discrete and continuous variable, and the big-five OCEAN personality traits as predictors, we utilize (a) multimodal behavioral cues, and (b) personality trait estimates obtained via these cues for hireability prediction (HP). For each of the text, audio and visual modalities, HP via (b) is found to be more effective than (a). Also, superior results are achieved when hireability is modeled as a continuous rather than a categorical variable. Interestingly, eye and bodily visual cues perform comparably to facial cues for predicting personality and hireability. Explanatory analyses reveal that multimodal behaviors impact personality and hireability impressions: e.g., Conscientiousness impressions are impacted by the use of positive adjectives (verbal behavior) and eye movements (non-verbal behavior), confirming prior observations

Hadronic contribution to the muon g-2: a Dyson-Schwinger perspective

We summarize our results for hadronic contributions to the anomalous magnetic moment of the muon ($a_\mu$), the one from hadronic vacuum-polarisation (HVP) and the light-by-light scattering contribution (LBL), obtained from the Dyson-Schwinger equations (DSE's) of QCD. In the case of HVP we find good agreement with model independent determinations from dispersion relations for $a_\mu^\mathrm{HVP}$ as well as for the Adler function with deviations well below the ten percent level. From this we conclude that the DSE approach should be capable of describing $a_\mu^\mathrm{LBL}$ with similar accuracy. We also present results for LBL using a resonance expansion of the quark anti-quark T-matrix. Our preliminary value is $a_\mu^\mathrm{LBL}=(217 \pm 91) \times 10^{-11}$.Comment: Contribution to the proceedings of 'International school of nuclear physics, 33rd course', Erice-Sicily: 16 - 24 September 201

Investigating word affect features and fusion of probabilistic predictions incorporating uncertainty in AVEC 2017

© 2017 Association for Computing Machinery. Predicting emotion intensity and severity of depression are both challenging and important problems within the broader field of affective computing. As part of the AVEC 2017, we developed a number of systems to accomplish these tasks. In particular, word affect features, which derive human affect ratings (e.g. arousal and valence) from transcripts, were investigated for predicting depression severity and liking, showing great promise. A simple system based on the word affect features achieved an RMSE of 6.02 on the test set, yielding a relative improvement of 13.6% over the baseline. For the emotion prediction sub-challenge, we investigated multimodal fusion, which incorporated a measure of uncertainty associated with each prediction within an Output-Associative fusion framework for arousal and valence prediction, whilst liking prediction systems mainly focused on text-based features. Our best emotion prediction systems provided significant relative improvements over the baseline on the test set of 39.5%, 17.6%, and 29.3% for arousal, valence, and liking. Of particular note is that consistent improvements were observed when incorporating prediction uncertainty across various system configurations for predicting arousal and valence, suggesting the importance of taking into consideration prediction uncertainty for fusion and more broadly the advantages of probabilistic predictions

Implications of LHC Searches on SUSY Particle Spectra: The pMSSM Parameter Space with Neutralino Dark Matter

We study the implications of LHC searches on SUSY particle spectra using flat scans of the 19-parameter pMSSM phase space. We apply constraints from flavour physics, g_mu-2, dark matter and earlier LEP and Tevatron searches. The sensitivity of the LHC SUSY searches with jets, leptons and missing energy is assessed by reproducing with fast simulation the recent CMS analyses after validation on benchmark points. We present results in terms of the fraction of pMSSM points compatible with all the constraints which are excluded by the LHC searches with 1 fb^{-1} and 15 fb^{-1} as a function of the mass of strongly and weakly interacting SUSY particles. We also discuss the suppression of Higgs production cross sections for the MSSM points not excluded and contrast the region of parameter space tested by the LHC data with the constraints from dark matter direct detection experiments.Comment: 14 pages, 13 figures. v2: increased statistics, to appear in EPJ

Hadronic Contributions to the Muon Anomaly in the Constituent Chiral Quark Model

The hadronic contributions to the anomalous magnetic moment of the muon which are relevant for the confrontation between theory and experiment at the present level of accuracy, are evaluated within the same framework: the constituent chiral quark model. This includes the contributions from the dominant hadronic vacuum polarization as well as from the next--to--leading order hadronic vacuum polarization, the contributions from the hadronic light-by-light scattering, and the contributions from the electroweak hadronic $Z\gamma\gamma$ vertex. They are all evaluated as a function of only one free parameter: the constituent quark mass. We also comment on the comparison between our results and other phenomenological evaluations.Comment: Several misprints corrected and a clarifying sentence added. Three figures superposed and two references added. Version to appear in JHE

Hadron shower decomposition in the highly granular CALICE analogue hadron calorimeter

The spatial development of hadronic showers in the CALICE scintillator-steel analogue hadron calorimeter is studied using test beam data collected at CERN and FNAL for single positive pions and protons with initial momenta in the range from 10 to 80 GeV/c. Both longitudinal and radial development of hadron showers are parametrised with two-component functions. The parametrisation is fit to test beam data and simulations using the QGSP_BERT and FTFP_BERT physics lists from Geant4 version 9.6. The parameters extracted from data and simulated samples are compared for the two types of hadrons. The response to pions and the ratio of the non-electromagnetic to the electromagnetic calorimeter response, h/e, are estimated using the extrapolation and decomposition of the longitudinal profiles.Comment: 38 pages, 19 figures, 5 tables; author list changed; submitted to JINS