Automatic 3D facial expression recognition using geometric and textured feature fusion

3D facial expression recognition has gained more and more interests from affective computing society due to issues such as pose variations and illumination changes caused by 2D imaging having been eliminated. There are many applications that can benefit from this research, such as medical applications involving the detection of pain and psychological effects in patients, in human-computer interaction tasks that intelligent systems use in today's world. In this paper, we look into 3D Facial Expression Recognition, by investigating many feature extraction methods used on the 2D textured images and 3D geometric data, fusing the 2 domains to increase the overall performance. A One Vs All Multi-class SVM Classifier has been adopted to recognize the expressions Angry, Disgust, Fear, Happy, Neutral, Sad and Surprise from the BU-3DFE and Bosphorus databases. The proposed approach displays an increase in performance when the features are fused together

Oxidative alkylation of (η5-C5Me5)2TiR (R = Cl, Me, Et, CH=CH2, Ph, OMe, N=C(H)tBu) to (η5-C5Me5)2Ti(Me)R by group 12 organometallic compounds MMe2

Oxidative alkylation of Cp*2TiX (Cp*: η5-C5Me5; X = OMe, Cl, N=C(H)tBu) and Cp* 2TiMe by CdMe2 or ZnMe2 gives diamagnetic Cp*2Ti(Me)X and Cp*2TiMe2 respectively, and cadmium or zinc. The reactions of Cp*2TiR (R = Et, CH=CH2, Ph) with MMe2 (M = Cd, Zn) give statistical mixtures of Cp*2Ti(Me)R, Cp*2TiMe2 and Cp*2TiR2. Dimethylmercury does not react with Cp*2TiX.

Calculations of critical misfit and thickness: An overview

This overview stresses the equilibrium/nonequilibrium nature of the physical properties, as well as the basic properties of the models, used to calculate critical misfit and critical thickness in epitaxy

Arbitrage Bounds for Prices of Weighted Variance Swaps

We develop robust pricing and hedging of a weighted variance swap when market prices for a finite number of co--maturing put options are given. We assume the given prices do not admit arbitrage and deduce no-arbitrage bounds on the weighted variance swap along with super- and sub- replicating strategies which enforce them. We find that market quotes for variance swaps are surprisingly close to the model-free lower bounds we determine. We solve the problem by transforming it into an analogous question for a European option with a convex payoff. The lower bound becomes a problem in semi-infinite linear programming which we solve in detail. The upper bound is explicit. We work in a model-independent and probability-free setup. In particular we use and extend F\"ollmer's pathwise stochastic calculus. Appropriate notions of arbitrage and admissibility are introduced. This allows us to establish the usual hedging relation between the variance swap and the 'log contract' and similar connections for weighted variance swaps. Our results take form of a FTAP: we show that the absence of (weak) arbitrage is equivalent to the existence of a classical model which reproduces the observed prices via risk-neutral expectations of discounted payoffs.Comment: 25 pages, 4 figure

Molecular cloning and characterization of a new member of the gap junction gene family, connexin-31

A new member of the connexin gene family has been identified and designated rat connexin-31 (Cx31) based on its predicted molecular mass of 30,960 daltons. Cx31 is 270 amino acids long and is coded for by a single copy gene. It is expressed as a 1.7-kilobase mRNA that is detected in placenta, Harderian gland, skin, and eye. Cx31 is highly conserved and can be detected in species as distantly related to rat as Xenopus laevis. It exhibits extensive sequence similarity to the previously identified connexins, 58, 50, and 40% amino acid identity to Cx26, Cx32, and Cx43, respectively. When conservation of predicted phosphorylation sites is used to adjust the alignment of Cx31 to other connexins, a unique alignment of three predicted protein kinase C phosphorylation sites near the carboxyl terminus of Cx31 with three sites at the carboxyl terminus of Cx43 is revealed

A Variational Formulation of Dissipative Quasicontinuum Methods

Lattice systems and discrete networks with dissipative interactions are successfully employed as meso-scale models of heterogeneous solids. As the application scale generally is much larger than that of the discrete links, physically relevant simulations are computationally expensive. The QuasiContinuum (QC) method is a multiscale approach that reduces the computational cost of direct numerical simulations by fully resolving complex phenomena only in regions of interest while coarsening elsewhere. In previous work (Beex et al., J. Mech. Phys. Solids 64, 154-169, 2014), the originally conservative QC methodology was generalized to a virtual-power-based QC approach that includes local dissipative mechanisms. In this contribution, the virtual-power-based QC method is reformulated from a variational point of view, by employing the energy-based variational framework for rate-independent processes (Mielke and Roub\'i\v{c}ek, Rate-Independent Systems: Theory and Application, Springer-Verlag, 2015). By construction it is shown that the QC method with dissipative interactions can be expressed as a minimization problem of a properly built energy potential, providing solutions equivalent to those of the virtual-power-based QC formulation. The theoretical considerations are demonstrated on three simple examples. For them we verify energy consistency, quantify relative errors in energies, and discuss errors in internal variables obtained for different meshes and two summation rules.Comment: 38 pages, 21 figures, 4 tables; moderate revision after review, one example in Section 5.3 adde

Automatic affective dimension recognition from naturalistic facial expressions based on wavelet filtering and PLS regression

Automatic affective dimension recognition from facial expression continuously in naturalistic contexts is a very challenging research topic but very important in human-computer interaction. In this paper, an automatic recognition system was proposed to predict the affective dimensions such as Arousal, Valence and Dominance continuously in naturalistic facial expression videos. Firstly, visual and vocal features are extracted from image frames and audio segments in facial expression videos. Secondly, a wavelet transform based digital filtering method is applied to remove the irrelevant noise information in the feature space. Thirdly, Partial Least Squares regression is used to predict the affective dimensions from both video and audio modalities. Finally, two modalities are combined to boost overall performance in the decision fusion process. The proposed method is tested in the fourth international Audio/Visual Emotion Recognition Challenge (AVEC2014) dataset and compared to other state-of-the-art methods in the affect recognition sub-challenge with a good performance