Identity-adaptive Facial Expression Recognition Through Expression Regeneration Using Conditional Generative Adversarial Networks

Subject variation is a challenging issue for facial expression recognition, especially when handling unseen subjects with small-scale labeled facial expression databases. Although transfer learning has been widely used to tackle the problem, the performance degrades on new data. In this paper, we present a novel approach (so-called IA-gen) to alleviate the issue of subject variations by regenerating expressions from any input facial images. First of all, we train conditional generative models to generate six prototypic facial expressions from any given query face image while keeping the identity related information unchanged. Generative Adversarial Networks are employed to train the conditional generative models, and each of them is designed to generate one of the prototypic facial expression images. Second, a regular CNN (FER-Net) is fine- tuned for expression classification. After the corresponding prototypic facial expressions are regenerated from each facial image, we output the last FC layer of FER-Net as features for both the input image and the generated images. Based on the minimum distance between the input image and the generated expression images in the feature space, the input image is classified as one of the prototypic expressions consequently. Our proposed method can not only alleviate the influence of inter-subject variations but will also be flexible enough to integrate with any other FER CNNs for person-independent facial expression recognition. Our method has been evaluated on CK+, Oulu-CASIA, BU-3DFE and BU-4DFE databases, and the results demonstrate the effectiveness of our proposed method

Numerical simulation on the impact of the bionic structure on aerodynamic noises of sidewindow regions in vehicles

The paper adopted a bionic hemispherical convex structure in the A pillar-rear view mirror regions according to actual requirements. Furthermore, impacts of the bionic structure on aerodynamic characteristics and noises in the region were studied. Friction resistance of airflows was greatly reduced, fluctuations and pulsation pressures of flow fields were also reduced, and characteristics of flow fields and sound fields were improved. The computational results were finally verified by the experimental test. Firstly, the aerodynamic lift force coefficient and drag force coefficient of the bionic model were computed, and they were obviously lower than those of the original model. The adhesive force between tires and ground during vehicle running was increased, and the danger degree of “waving” of high-speed vehicle running was weakened. In this way, stability of vehicle running could be improved. Secondly, flow fields of the bionic model were computed. Compared with the original model, an obvious vortex was behind the original model, while no vortexes were behind the bionic model. Therefore, convex structures of the bionic model had obvious impacts on flow fields behind the rear view mirror. Airflow separation situations were obvious improved at wheels, windshield and rear side windows of the bionic model. Due to blocking of convex structures of the A pillar and rear view mirror in the bionic model, airflows was hindered and obvious dragging phenomena were formed. Therefore, flow fields in the side window regions could be improved greatly. In addition, the flow field scope under the rear view mirror in the bionic model was also decreased. Ringed vortex structures appeared behind the rear view mirror in the bionic model. The ringed vortex structures were closely interlaced and then extended together backwards. Vortexes behind the rear view mirror in the original model were chaotic, where most of them were attached on the surface of side windows. In the original model, turbulent flows with certain strength were on the right upper corner of the side window region. In the bionic model, no turbulent flows were in the same regions. This result indicated that through using the bionic convex structures, airflows flowing through side windows could be combed and could move backwards towards upper and lower edges of the side windows. It could be predicted that pulsation pressures on the side window surface would surely decrease. Therefore, aerodynamic noises caused by pulsation pressures in side window regions would also be improved correspondingly. Especially in regions behind A pillar-rear view mirrors, the maximum noise reduction amplitude reached about 20 dB

Global weak sharp minima for convex (semi-)infinite optimization problems

AbstractWe mainly consider global weak sharp minima for convex infinite and semi-infinite optimization problems (CIP). In terms of the normal cone, subdifferential and directional derivative, we provide several characterizations for (CIP) to have global weak sharp minimum property

Multi-modality Empowered Network For Facial Action Unit Detection

This paper presents a new thermal empowered multi-task network (TEMT-Net) to improve facial action unit detection. Our primary goal is to leverage the situation that the training set has multi-modality data while the application scenario only has one modality. Thermal images are robust to illumination and face color. In the proposed multi-task framework, we utilize both modality data. Action unit detection and facial landmark detection are correlated tasks. To utilize the advantage and the correlation of different modalities and different tasks, we propose a novel thermal empowered multi-task deep neural network learning approach for action unit detection, facial landmark detection and thermal image reconstruction simultaneously. The thermal image generator and facial landmark detection provide regularization on the learned features with shared factors as the input color images. Extensive experiments are conducted on the BP4D and MMSE databases, with the comparison to the state-of-the-art methods. The experiments show that the multi-modality framework improves the AU detection significantly

Effect of process factors on solidification process of 10B21 steel bloom

In this study, Finite Element Method (FEM) was used to simulate the solidification process of a large blank (280 mm × 325 mm) under different technological conditions. The influence of casting speed and superheat on solidification process of bloom was analyzed. It was found that as the casting speed increases, the solidification position of the bloom moved backward by 3,68 m, and the time required for complete solidification increased by 1 min; When the superheat gradually increased, the position of complete solidification of 10B21 steel bloom moved by about 1,5 m

Effect of process factors on solidification process of 10B21 steel bloom

In this study, Finite Element Method (FEM) was used to simulate the solidification process of a large blank (280 mm × 325 mm) under different technological conditions. The influence of casting speed and superheat on solidification process of bloom was analyzed. It was found that as the casting speed increases, the solidification position of the bloom moved backward by 3,68 m, and the time required for complete solidification increased by 1 min; When the superheat gradually increased, the position of complete solidification of 10B21 steel bloom moved by about 1,5 m