2 research outputs found
Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution
The intensity estimation of facial action units (AUs) is challenging due to
subtle changes in the person's facial appearance. Previous approaches mainly
rely on probabilistic models or predefined rules for modeling co-occurrence
relationships among AUs, leading to limited generalization. In contrast, we
present a new learning framework that automatically learns the latent
relationships of AUs via establishing semantic correspondences between feature
maps. In the heatmap regression-based network, feature maps preserve rich
semantic information associated with AU intensities and locations. Moreover,
the AU co-occurring pattern can be reflected by activating a set of feature
channels, where each channel encodes a specific visual pattern of AU. This
motivates us to model the correlation among feature channels, which implicitly
represents the co-occurrence relationship of AU intensity levels. Specifically,
we introduce a semantic correspondence convolution (SCC) module to dynamically
compute the correspondences from deep and low resolution feature maps, and thus
enhancing the discriminability of features. The experimental results
demonstrate the effectiveness and the superior performance of our method on two
benchmark datasets.Comment: Accepted at AAAI202
An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge
Facial micro-expressions indicate brief and subtle facial movements that
appear during emotional communication. In comparison to macro-expressions,
micro-expressions are more challenging to be analyzed due to the short span of
time and the fine-grained changes. In recent years, micro-expression
recognition (MER) has drawn much attention because it can benefit a wide range
of applications, e.g. police interrogation, clinical diagnosis, depression
analysis, and business negotiation. In this survey, we offer a fresh overview
to discuss new research directions and challenges these days for MER tasks. For
example, we review MER approaches from three novel aspects: macro-to-micro
adaptation, recognition based on key apex frames, and recognition based on
facial action units. Moreover, to mitigate the problem of limited and biased ME
data, synthetic data generation is surveyed for the diversity enrichment of
micro-expression data. Since micro-expression spotting can boost
micro-expression analysis, the state-of-the-art spotting works are also
introduced in this paper. At last, we discuss the challenges in MER research
and provide potential solutions as well as possible directions for further
investigation.Comment: 20 pages, 7 figure