Facial Motion Prior Networks for Facial Expression Recognition

Deep learning based facial expression recognition (FER) has received a lot of attention in the past few years. Most of the existing deep learning based FER methods do not consider domain knowledge well, which thereby fail to extract representative features. In this work, we propose a novel FER framework, named Facial Motion Prior Networks (FMPN). Particularly, we introduce an addition branch to generate a facial mask so as to focus on facial muscle moving regions. To guide the facial mask learning, we propose to incorporate prior domain knowledge by using the average differences between neutral faces and the corresponding expressive faces as the training guidance. Extensive experiments on three facial expression benchmark datasets demonstrate the effectiveness of the proposed method, compared with the state-of-the-art approaches.Comment: VCIP 2019, Oral. Code is available at https://github.com/donydchen/FMPN-FE

Kervolutional Neural Networks

Convolutional neural networks (CNNs) have enabled the state-of-the-art performance in many computer vision tasks. However, little effort has been devoted to establishing convolution in non-linear space. Existing works mainly leverage on the activation layers, which can only provide point-wise non-linearity. To solve this problem, a new operation, kervolution (kernel convolution), is introduced to approximate complex behaviors of human perception systems leveraging on the kernel trick. It generalizes convolution, enhances the model capacity, and captures higher order interactions of features, via patch-wise kernel functions, but without introducing additional parameters. Extensive experiments show that kervolutional neural networks (KNN) achieve higher accuracy and faster convergence than baseline CNN.Comment: oral paper in CVPR 201

A study of smart device-based mobile imaging and implementation for engineering applications

Title from PDF of title page, viewed on June 12, 2013Thesis advisor: ZhiQiang ChenVitaIncludes bibliographic references (pages 76-82)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2013Mobile imaging has become a very active research topic in recent years thanks to the rapid development of computing and sensing capabilities of mobile devices. This area features multi-disciplinary studies of mobile hardware, imaging sensors, imaging and vision algorithms, wireless network and human-machine interface problems. Due to the limitation of computing capacity that early mobile devices have, researchers proposed client-server module, which push the data to more powerful computing platforms through wireless network, and let the cloud or standalone servers carry out all the computing and processing work. This thesis reviewed the development of mobile hardware and software platform, and the related research done on mobile imaging for the past 20 years. There are several researches on mobile imaging, but few people aim at building a framework which helps engineers solving problems by using mobile imaging. With higher-resolution imaging and high-performance computing power built into smart mobile devices, more and more imaging processing tasks can be achieved on the device rather than the client-server module. Based on this fact, a framework of collaborative mobile imaging is introduced for civil infrastructure condition assessment to help engineers solving technical challenges. Another contribution in this thesis is applying mobile imaging application into home automation. E-SAVE is a research project focusing on extensive use of automation in conserving and using energy wisely in home automation. Mobile users can view critical information such as energy data of the appliances with the help of mobile imaging. OpenCV is an image processing and computer vision library. The applications in this thesis use functions in OpenCV including camera calibration, template matching, image stitching and Canny edge detection. The application aims to help field engineers is interactive crack detection. The other one uses template matching to recognize appliances in the home automation system.Introduction -- Background and related work -- Basic imaging processing methods for mobile applications -- Collaborative and interactive mobile imaging -- Mobile imaging for smart energy -- Conclusion and recommendation