Micro-Expression Recognition Using Uncertainty-Aware Magnification-Robust Networks.

A micro-expression (ME) is a kind of involuntary facial expressions, which commonly occurs with subtle intensity. The accurately recognition ME, a. k. a. micro-expression recognition (MER), has a number of potential applications, e.g., interrogation and clinical diagnosis. Therefore, the subject has received a high level of attention among researchers in affective computing and pattern recognition communities. In this paper, we proposed a straightforward and effective deep learning method called uncertainty-aware magnification-robust networks (UAMRN) for MER, which attempts to address two key issues in MER including the low intensity of ME and imbalance of ME samples. Specifically, to better distinguish subtle ME movements, we reconstructed a new sequence by magnifying the ME intensity. Furthermore, a sparse self-attention (SSA) block was implemented which rectifies the standard self-attention with locality sensitive hashing (LSH), resulting in the suppression of artefacts generated during magnification. On the other hand, for the class imbalance problem, we guided the network optimization based on the confidence about the estimation, through which the samples from rare classes were allotted greater uncertainty and thus trained more carefully. We conducted the experiments on three public ME databases, i.e., CASME II, SAMM and SMIC-HS, the results of which demonstrate improvement compared to recent state-of-the-art MER methods.

Enhanced Sample Self-Revised Network for Cross-Dataset Facial Expression Recognition.

Recently, cross-dataset facial expression recognition (FER) has obtained wide attention from researchers. Thanks to the emergence of large-scale facial expression datasets, cross-dataset FER has made great progress. Nevertheless, facial images in large-scale datasets with low quality, subjective annotation, severe occlusion, and rare subject identity can lead to the existence of outlier samples in facial expression datasets. These outlier samples are usually far from the clustering center of the dataset in the feature space, thus resulting in considerable differences in feature distribution, which severely restricts the performance of most cross-dataset facial expression recognition methods. To eliminate the influence of outlier samples on cross-dataset FER, we propose the enhanced sample self-revised network (ESSRN) with a novel outlier-handling mechanism, whose aim is first to seek these outlier samples and then suppress them in dealing with cross-dataset FER. To evaluate the proposed ESSRN, we conduct extensive cross-dataset experiments across RAF-DB, JAFFE, CK+, and FER2013 datasets. Experimental results demonstrate that the proposed outlier-handling mechanism can reduce the negative impact of outlier samples on cross-dataset FER effectively and our ESSRN outperforms classic deep unsupervised domain adaptation (UDA) methods and the recent state-of-the-art cross-dataset FER results.

Attentional focus modulates automatic finger-tapping movements.

The majority of human behaviors are composed of automatic movements (e.g., walking or finger-tapping) which are learned during nurturing and can be performed simultaneously without interfering with other tasks. One critical and yet to be examined assumption is that the attention system has the innate capacity to modulate automatic movements. The present study tests this assumption. Setting no deliberate goals for movement, we required sixteen participants to perform personalized and well-practiced finger-tapping movements in three experiments while focusing their attention on either different component fingers or away from movements. Using cutting-edge pose estimation techniques to quantify tapping trajectory, we showed that attention to movement can disrupt movement automaticity, as indicated by decreased inter-finger and inter-trial temporal coherence; facilitate the attended and inhibit the unattended movements in terms of tapping amplitude; and re-organize the action sequence into distinctive patterns according to the focus of attention. These findings demonstrate compelling evidence that attention can modulate automatic movements and provide an empirical foundation for theories based on such modulation in controlling human behavior.