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Region attention and graph embedding network for occlusion objective class-based micro-expression recognition

arXiv.org Artificial Intelligence

Micro-expression recognition (\textbf{MER}) has attracted lots of researchers' attention in a decade. However, occlusion will occur for MER in real-world scenarios. This paper deeply investigates an interesting but unexplored challenging issue in MER, \ie, occlusion MER. First, to research MER under real-world occlusion, synthetic occluded micro-expression databases are created by using various mask for the community. Second, to suppress the influence of occlusion, a \underline{R}egion-inspired \underline{R}elation \underline{R}easoning \underline{N}etwork (\textbf{RRRN}) is proposed to model relations between various facial regions. RRRN consists of a backbone network, the Region-Inspired (\textbf{RI}) module and Relation Reasoning (\textbf{RR}) module. More specifically, the backbone network aims at extracting feature representations from different facial regions, RI module computing an adaptive weight from the region itself based on attention mechanism with respect to the unobstructedness and importance for suppressing the influence of occlusion, and RR module exploiting the progressive interactions among these regions by performing graph convolutions. Experiments are conducted on handout-database evaluation and composite database evaluation tasks of MEGC 2018 protocol. Experimental results show that RRRN can significantly explore the importance of facial regions and capture the cooperative complementary relationship of facial regions for MER. The results also demonstrate RRRN outperforms the state-of-the-art approaches, especially on occlusion, and RRRN acts more robust to occlusion.


Ken Denman, Emotient: our micro-expressions reveal what you're really thinking

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"Since the beginning of time we've failed to understand how people feel," Ken Denman, president and CEO of engagement and emotion analysis firm Emotient told the audience at WIRED Retail. "The reality is, we've just been guessing." WIRED Retail is our annual exploration of the ever-changing world of commerce, featuring leading technologists, entrepreneurs and creatives innovating in sectors as diverse as robotics, virtual reality and the future of home delivery.


Micro-Expression Recognition Based on Pixel Residual Sum and Cropped Gaussian Pyramid

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Facial micro-expression(ME) recognition has great significance for the progress of human society and could find person's true feelings. Meanwhile, ME recognition faces a huge challenge, since it is difficult to detect and easy to be disturbed by the environment. In this paper, we propose two novel preprocessing methods based on Pixel Residual Sum. These methods can preprocess video clips according to the unit pixel displacement of images, resist environmental interference and be easy to extract subtle facial feature. Furthermore, we propose a Cropped Gaussian Pyramid with Overlapping(CGPO) module, which divides images of different resolutions through Gaussian pyramids and crops different resolutions image into multiple overlapping subplot. Then, we use a convolutional network of progressively increasing channels based on the depthwise convolution to extract preliminary features. Finally, we fuse preliminary features and make position embedding to get last features. Our experiments show that the proposed methods and model have better performance than the well-known methods.


Why is it hard for AI to detect human bias?

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AI bias is in the news – and it's a hard problem to solve When AI engages with humans – how does AI know what humans really means? In other words, why is it hard for AI to detect human bias? That's because humans do not say what they really mean due to factors such as cognitive dissonance. Cognitive dissonance refers to a situation involving conflicting attitudes, beliefs or behaviours. This produces a feeling of mental discomfort leading to an alteration in one of the attitudes, beliefs or behaviours to reduce the discomfort and restore balance.


Scientists discover lie detector that uses artificial intelligence to detect micro-expressions

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In this file photo, a representative image of Artificial Intelligence can be seen. Scientists have discovered a new lie detector that can read facial muscles that people won't even know they are using. The study, conducted by the researchers at Tel Aviv University, has been in'Brain and Behaviour.' It was conducted on the basis of micro-expressions that vanish in 40 to 60 milliseconds due to which accuracy and speed played a key role. Also read Experts look to recycle dangerous space junk into rocket fuel in Earth's orbit ''Since this was an initial study, the lie itself was very simple,'' he added.