Micro-expressions (MEs) are involuntary facial movements revealing people's hidden feelings in high-stake situations and have practical importance in various fields. Early methods for Micro-expression Recognition (MER) are mainly based on traditional features. Recently, with the success of Deep Learning (DL) in various tasks, neural networks have received increasing interest in MER. Different from macro-expressions, MEs are spontaneous, subtle, and rapid facial movements, leading to difficult data collection and annotation, thus publicly available datasets are usually small-scale. Currently, various DL approaches have been proposed to solve the ME issues and improve MER performance. In this survey, we provide a comprehensive review of deep MER and define a new taxonomy for the field encompassing all aspects of MER based on DL, including datasets, each step of the deep MER pipeline, and performance comparisons of the most influential methods. The basic approaches and advanced developments are summarized and discussed for each aspect. Additionally, we conclude the remaining challenges and potential directions for the design of robust MER systems. Finally, ethical considerations in MER are discussed. To the best of our knowledge, this is the first survey of deep MER methods, and this survey can serve as a reference point for future MER research.

No abstract

Facial image inpainting is a challenging task because the missing region needs to be filled by the new pixels with semantic information (e.g., noses and mouths). The traditional methods that involve searching for similar patches are mature but it is not suitable for semantic inpainting. Recently, the deep generative model-based methods have been able to implement semantic image inpainting although inpainting results are blurry or distorted. In this paper, through analyzing the advantages and disadvantages of the two methods, we propose a novel and efficient method that combines these two methods by a series connection, which searches for the most reasonable similar patch using the coarse image generated by the deep generative model. When training model, adding Laplace loss to standard loss accelerates model convergence. In addition, we define region weight (RW) when searching for similar patches, which makes edge connection more natural. Our method addresses the problem of blurred results in the deep generative model and dissatisfactory semantic information in the traditional methods. Our experiments, which used the CelebA dataset, demonstrate that our method can achieve realistic and natural facial inpainting results.INDEX TERMS Facial image inpainting, deep generative model, similar patch, region weight. I. INTRODUCTION

No abstract

No abstract