This paper focuses on the research of micro-expression recognition (MER) and proposes a flexible and reliable deep learning method called learning to rank onset-occurring-offset representations (LTR3O).
Anatomically, there are innumerable correlations between AUs, which contain rich information and are vital for AU detection.
As key modules in PhysFormer, the temporal difference transformers first enhance the quasi-periodic rPPG features with temporal difference guided global attention, and then refine the local spatio-temporal representation against interference.
Remote photoplethysmography (rPPG) enables non-contact heart rate (HR) estimation from facial videos which gives significant convenience compared with traditional contact-based measurements.
In this paper, we establish the first joint face spoofing and forgery detection benchmark using both visual appearance and physiological rPPG cues.
We propose UWash, an intelligent solution upon smartwatches, to assess handwashing for the purpose of raising users' awareness and cultivating habits in high-quality handwashing.
Remote photoplethysmography (rPPG), which aims at measuring heart activities and physiological signals from facial video without any contact, has great potential in many applications (e. g., remote healthcare and affective computing).
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from the presentation attacks (PAs).
In human action recognition, current works introduce a dynamic graph generation mechanism to better capture the underlying semantic skeleton connections and thus improves the performance.
Ranked #40 on Skeleton Based Action Recognition on NTU RGB+D 120
In this paper we rephrase face anti-spoofing as a material recognition problem and combine it with classical human material perception , intending to extract discriminative and robust features for FAS.
Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications (e. g., remote healthcare).
We fuse face, upperbody and scene information for robustness of GER against the challenging environments.
Group sparsity has shown great potential in various low-level vision tasks (e. g, image denoising, deblurring and inpainting).