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

Flight test results from a supercritical mission adaptive wing with smooth variable camber

The mission adaptive wing (MAW) consisted of leading- and trailing-edge variable-camber surfaces that could be deflected in flight to provide a near-ideal wing camber shape for any flight condition. These surfaces featured smooth, flexible upper surfaces and fully enclosed lower surfaces, distinguishing them from conventional flaps that have discontinuous surfaces and exposed or semiexposed mechanisms. Camber shape was controlled by either a manual or automatic flight control system. The wing and aircraft were extensively instrumented to evaluate the local flow characteristics and the total aircraft performance. This paper discusses the interrelationships between the wing pressure, buffet, boundary-layer and flight deflection measurement system analyses and describes the flight maneuvers used to obtain the data. The results are for a wing sweep of 26 deg, a Mach number of 0.85, leading and trailing-edge cambers (delta(sub LE/TE)) of 0/2 and 5/10, and angles of attack from 3.0 deg to 14.0 deg. For the well-behaved flow of the delta(sub LE/TE) = 0/2 camber, a typical cruise camber shape, the local and global data are in good agreement with respect to the flow properties of the wing. For the delta(sub LE/TE) = 5/10 camber, a maneuvering camber shape, the local and global data have similar trends and conclusions, but not the clear-cut agreement observed for cruise camber

Non-contact technique for characterizing full-field surface deformation of shape memory polymers at elevated and room temperatures

Abstract Thermally activated shape memory polymers (SMPs) can display modulus changes of approximately three orders of magnitude in transitioning from the high modulus, "glassy" state below the glass transition temperature (Tg) to the low modulus, "rubbery" state above the Tg. In the high temperature region, SMPs can achieve strain levels well above 100%. Their complex behavior includes large modulus changes to as low as ∼1 MPa, extremely high strain levels, and path dependent properties, thus precluding the use of traditional strain gages and low-contact force extensometers. The present study presents a comparison of thermomechanical testing techniques developed to characterize the material behavior of SMPs. Specifically, the performance of strain measurements using contact methods (clip-on extensometers and adhesive strain gages) are compared to non-contact methods (laser extensometer and digital image correlation). An MTS environmental chamber with an observation window allows for non-contact optical measurements during testing. A series of tensile tests are performed on a commercial SMP (with a Tg of ∼105 °C) at 25 °C and at 130 °C. It is observed that the clip-on extensometer significantly affects the SMP behavior even in the low temperature, high modulus state. Overall, the laser extensometer provides a robust method for controlling the axial strain in the gage section of the samples at moderate strain rates. The digital image correlation allows for full field measurement of both axial and transverse strains of SMPs over a range of temperatures and strain rates

Gap Bridging Ability in Laser GMA Hybrid Welding of Thin 22MnB5 Sheets

AbstractIn this paper, laser GMA hybrid welding of thin ultra-high-strength steel sheets (22MnB5) is investigated. A single-mode laser beam oscillating transversal to the welding direction is used in order to minimize the heat input during the process. The sheets have a thickness of 1.5mm each and are fixed in overlap configuration. The gap between the sheets was 0.8mm during experiments in order to simulate typical gap width in industrial manufacturing processes. It is shown that a stable weld seam has been achieved for this gap width in case of a welding speed of 6m/min. The gap bridging ability is caused by the interaction of the arc and the laser beam process. The laser beam process produces deeper penetration in the bottom sheet. Thus, the arc is stabilized by the laser beam

Efficient Labelling of Affective Video Datasets via Few-Shot & Multi-Task Contrastive Learning

Whilst deep learning techniques have achieved excellent emotion prediction, they still require large amounts of labelled training data, which are (a) onerous and tedious to compile, and (b) prone to errors and biases. We propose Multi-Task Contrastive Learning for Affect Representation (MT-CLAR) for few-shot affect inference. MT-CLAR combines multi-task learning with a Siamese network trained via contrastive learning to infer from a pair of expressive facial images (a) the (dis)similarity between the facial expressions, and (b) the difference in valence and arousal levels of the two faces. We further extend the image-based MT-CLAR framework for automated video labelling where, given one or a few labelled video frames (termed support-set), MT-CLAR labels the remainder of the video for valence and arousal. Experiments are performed on the AFEW-VA dataset with multiple support-set configurations; moreover, supervised learning on representations learnt via MT-CLAR are used for valence, arousal and categorical emotion prediction on the AffectNet and AFEW-VA datasets. The results show that valence and arousal predictions via MT-CLAR are very comparable to the state-of-the-art (SOTA), and we significantly outperform SOTA with a support-set ≈6% the size of the video dataset

From individual to group-level emotion recognition: Emoti W 5.0

Research in automatic affect recognition has come a long way. This paper describes the fifth Emotion Recognition in the Wild (EmotiW) challenge 2017. EmotiW aims at providing a common benchmarking platform for researchers working on different aspects of affective computing. This year there are two sub-challenges: A) Audio-video emotion recognition and b) group-level emotion recognition. These challenges are based on the acted facial expressions in the wild and group affect databases, respectively. The particular focus of the challenge is to evaluate method in 'in the wild' settings. 'In the wild' here is used to describe the various environments represented in the images and videos, which represent real-world (not lab like) scenarios. The baseline, data, protocol of the two challenges and the challenge participation are discussed in detail in this paper

Removal of xenoantigenic glycosylation patterns from porcine pulmonary heart valve matrices is dependent of the applied decellularization method

Department of Cardiac-, Thoracic-, Transplantation and Vascular Surgery, Hannover Medical School, Hannover Germany and Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Hannover Medical School, Hannover, Germany, The 6th International Medical Congress for Students and Young Doctors, May 12-14, 2016Introduction: Matrix guided tissue regeneration (GTR) based on allogeneic decellularized matrices has been shown as an overall convincing method for heart valve replacement. Nevertheless, a substantial donor shortage prevents an unlimited clinical application of human GTR-valves. Utilization of porcine decellularized heart valve matrices could offer a possible solution for overcoming this considerable limitation. In the past, implantation of xenogeneic valve tissues considered to be acellularinto human recipients, however, mostly lead to severe immune responses usually ending up into graft rejection. This study addresses the question whether potential xenoantigenic glycosylation of extracellular matrix components, like the major xenoantigen α-Gal, which served as model epitope for this study, can be removed by adjusted decellularization procedures. Materials and methods: Fresh porcine pulmonary heart valve conduits were decellularized by application of different detergent- and enzyme-based decellularization protocols. Subsequent cleavage of remaining matrix-related α-Gal epitopes was performed by enzymatic deglycosylation treatment on matrix samples of each decellularization group. Resulting tissues, mainly composed from insoluble extracellular matrix proteins, were afterwards divided into the relevant sections pulmonary artery wall specimens and pulmonary valve leaflets, frozen in liquid nitrogen, minced and finally solubilized by protease digestion. Evaluation of thus prepared solutions regarding to α-Gal contents was finally performed using a novel designed lectin-based immunoblot technique. Discussion results: Sole decellularization lead to significant removal of α-Gal, substantial varying in strong dependency to applied protocols between 30 to 50% compared to α-Gal contents of porcine native control tissues. An additional decrease of residual α-Gal in a range of another 15 to 30% was achievable by additional α-Galactosidase treatment. Combining decellularization and subsequent enzymatic digestion resulted in reductions of matrix related α-Gal contents down to levels, which could be measured for respective pulmonary valve tissues of α-Gal-KnockOut pigs. Conclusion: Residual xenoantigenic carbohydrates are detectable on insoluble matrix components of porcine pulmonary heart valves, substantially varying dependent on applied decellularization protocols. Combined with glycolytic digestions, remaining glycosylation contents are reducible to background levels. Impacts of these novel insights have to be evaluated in further in vitro as well as in vivo studies

Emotion recognition in the wild

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