Spontaneous Subtle Expression Detection and Recognition based on Facial Strain

Optical strain is an extension of optical flow that is capable of quantifying subtle changes on faces and representing the minute facial motion intensities at the pixel level. This is computationally essential for the relatively new field of spontaneous micro-expression, where subtle expressions can be technically challenging to pinpoint. In this paper, we present a novel method for detecting and recognizing micro-expressions by utilizing facial optical strain magnitudes to construct optical strain features and optical strain weighted features. The two sets of features are then concatenated to form the resultant feature histogram. Experiments were performed on the CASME II and SMIC databases. We demonstrate on both databases, the usefulness of optical strain information and more importantly, that our best approaches are able to outperform the original baseline results for both detection and recognition tasks. A comparison of the proposed method with other existing spatio-temporal feature extraction approaches is also presented.Comment: 21 pages (including references), single column format, accepted to Signal Processing: Image Communication journa

Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification

Stacking-based deep neural network (S-DNN) is aggregated with pluralities of basic learning modules, one after another, to synthesize a deep neural network (DNN) alternative for pattern classification. Contrary to the DNNs trained end to end by backpropagation (BP), each S-DNN layer, i.e., a self-learnable module, is to be trained decisively and independently without BP intervention. In this paper, a ridge regression-based S-DNN, dubbed deep analytic network (DAN), along with its kernelization (K-DAN), are devised for multilayer feature re-learning from the pre-extracted baseline features and the structured features. Our theoretical formulation demonstrates that DAN/K-DAN re-learn by perturbing the intra/inter-class variations, apart from diminishing the prediction errors. We scrutinize the DAN/K-DAN performance for pattern classification on datasets of varying domains - faces, handwritten digits, generic objects, to name a few. Unlike the typical BP-optimized DNNs to be trained from gigantic datasets by GPU, we disclose that DAN/K-DAN are trainable using only CPU even for small-scale training sets. Our experimental results disclose that DAN/K-DAN outperform the present S-DNNs and also the BP-trained DNNs, including multiplayer perceptron, deep belief network, etc., without data augmentation applied.Comment: 14 pages, 7 figures, 11 table

Multiple-antenna-aided OFDM employing genetic-algorithm-assisted minimum bit error rate multiuser detection

The family of minimum bit error rate (MBER) multiuser detectors (MUD) is capable of outperforming the classic minimum mean-squared error (MMSE) MUD in terms of the achievable bit-error rate (BER) owing to directly minimizing the BER cost function. In this paper,wewill invoke genetic algorithms (GAs) for finding the optimum weight vectors of the MBER MUD in the context of multiple-antenna-aided multiuser orthogonal frequency division multiplexing (OFDM) .We will also show that the MBER MUD is capable of supporting more users than the number of receiver antennas available, while outperforming the MMSE MUD

Avalanche noise characteristics of thin GaAs structures with distributed carrier generation

It is known that both pure electron and pure hole injection into thin GaAs multiplication regions gives rise to avalanche multiplication with noise lower than predicted by the local noise model. In this paper, it is shown that the noise from multiplication initiated by carriers generated throughout a 0.1 μm avalanche region is also lower than predicted by the local model but higher than that obtained with pure injection of either carrier type. This behavior is due to the effects of nonlocal ionization brought about by the dead space; the minimum distance a carrier has to travel in the electric field to initiate an ionization even

Subband decomposition techniques for adaptive channel equalisation

In this contribution, the convergence behaviour of the adaptive linear equaliser based on subband decomposition technique is investigated. Two different subband-based linear equalisers are employed, with the aim of improving the equaliser's convergence performance. Simulation results over three channel models having different spectral characteristic are presented. Computer simulations indicate that subband-based equalisers outperform the conventional fullband linear equaliser when channel exhibit severe spectral dynamic. Convergence rate of subband equalisers are governed by the slowest subband, whereby different convergence behaviour in each individual subband is observed. Finally, the complexity of fullband and subband equalisers is discussed

Quantum-implemented selective reconstruction of high-resolution images

This paper proposes quantum image reconstruction. Input-triggered selection of an image among many stored ones, and its reconstruction if the input is occluded or noisy, has been simulated by a computer program implementable in a real quantum-physical system. It is based on the Hopfield associative net; the quantum-wave implementation bases on holography. The main limitations of the classical Hopfield net are much reduced with the new, original -- quantum-optical -- implementation. Image resolution can be almost arbitrarily increased.Comment: 4 pages, 15 figures, essential

Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis \& Application

Spontaneous subtle emotions are expressed through micro-expressions, which are tiny, sudden and short-lived dynamics of facial muscles; thus poses a great challenge for visual recognition. The abrupt but significant dynamics for the recognition task are temporally sparse while the rest, irrelevant dynamics, are temporally redundant. In this work, we analyze and enforce sparsity constrains to learn significant temporal and spectral structures while eliminate irrelevant facial dynamics of micro-expressions, which would ease the challenge in the visual recognition of spontaneous subtle emotions. The hypothesis is confirmed through experimental results of automatic spontaneous subtle emotion recognition with several sparsity levels on CASME II and SMIC, the only two publicly available spontaneous subtle emotion databases. The overall performances of the automatic subtle emotion recognition are boosted when only significant dynamics are preserved from the original sequences.Comment: IEEE Transaction of Affective Computing (2016

Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition

Facial micro-expression (ME) recognition has posed a huge challenge to researchers for its subtlety in motion and limited databases. Recently, handcrafted techniques have achieved superior performance in micro-expression recognition but at the cost of domain specificity and cumbersome parametric tunings. In this paper, we propose an Enriched Long-term Recurrent Convolutional Network (ELRCN) that first encodes each micro-expression frame into a feature vector through CNN module(s), then predicts the micro-expression by passing the feature vector through a Long Short-term Memory (LSTM) module. The framework contains two different network variants: (1) Channel-wise stacking of input data for spatial enrichment, (2) Feature-wise stacking of features for temporal enrichment. We demonstrate that the proposed approach is able to achieve reasonably good performance, without data augmentation. In addition, we also present ablation studies conducted on the framework and visualizations of what CNN "sees" when predicting the micro-expression classes.Comment: Published in Micro-Expression Grand Challenge 2018, Workshop of 13th IEEE Facial & Gesture 201

Towards Accurate One-Stage Object Detection with AP-Loss

One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme foreground-background class imbalance issue due to the large number of anchors. This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the Average-Precision loss (AP-loss) for the ranking problem. Due to its non-differentiability and non-convexity, the AP-loss cannot be optimized directly. For this purpose, we develop a novel optimization algorithm, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks. We verify good convergence property of the proposed algorithm theoretically and empirically. Experimental results demonstrate notable performance improvement in state-of-the-art one-stage detectors based on AP-loss over different kinds of classification-losses on various benchmarks, without changing the network architectures. Code is available at https://github.com/cccorn/AP-loss.Comment: 13 pages, 7 figures, 4 tables, main paper + supplementary material, accepted to CVPR 201

First-principles investigation of graphitic carbon nitride monolayer with embedded Fe atom

Density-functional theory calculations with spin-polarized generalized gradient approximation and Hubbard $U$ correction is carried out to investigate the mechanical, structural, electronic and magnetic properties of graphitic heptazine with embedded $\mathrm{Fe}$ atom under bi-axial tensile strain and applied perpendicular electric field. It was found that the binding energy of heptazine with embedded $\mathrm{Fe}$ atom system decreases as more tensile strain is applied and increases as more electric field strength is applied. Our calculations also predict a band gap at a peak value of 5 tensile strain but at expense of the structural stability of the system. The band gap opening at 5 tensile strain is due to distortion in the structure caused by the repulsive effect in the cavity between the lone pairs of edge nitrogen atoms and $\mathrm{d}_{{xy}}/\mathrm{d}_{x^2-y^2}$ orbital of Fe atom, hence the unoccupied $\mathrm{p}_z$-orbital is forced to shift towards higher energy. The electronic and magnetic properties of the heptazine with embedded $\mathrm{Fe}$ system under perpendicular electric field up to a peak value of 10 $\mathrm{V/nm}$ is also well preserved despite obvious buckled structure. Such properties may be desirable for diluted magnetic semiconductors, spintronics, and sensing devices