The ugly gossip about Marjorie Taylor Greene swirling in DC... no wonder she's giving this'nothing to see here' performance of a lifetime: KENNEDY Tupac's family hid his final secret for decades. Southern city morphs into New York's'tiny twin' as Big Apple residents flock there in droves to escape woke mayor The truth about Aaron Rodgers's secret'wife': Family lift the lid on the NFL's biggest mystery... and finally put to bed those swirling rumors Singer Grande shows off her 40 hand'prison' tattoos at Wicked: For Good premiere in Paris Insiders blow lid on top secret actor'blacklist' at Paramount that's tearing Hollywood apart and start naming names White House space sabotage plot EXPOSED: The truth behind the NASA war that tore Trump's inner circle in two Wild image shows how Simone Biles would look next to Olivier Rioux... after he made his college basketball debut Donald Trump wants Washington Commanders to name $3.7billion stadium after him Air India grounds three Boeing planes for'extensive investigations' after crash that killed 260 She was an award-winning Teacher of the Year. Succession star Sarah Snook's new thriller is the best show of the year - its brings every parent's worst nightmare to life in spectacular fashion and I binged all eight episodes in one sitting Fears as Days of Our Lives is beset by string of tragedies... leaving producers desperately scrambling to save iconic show Soap icon turned ordained minister who flirted with Andy Warhol steps out in LA... can you guess who? Jeremy Renner's film partner claims he sent her explicit photos and videos to woo her then threatened the unthinkable when they fell out Whether you're in a work meeting or on a first date, it can sometimes be impossible to tell what someone is thinking. But help is at hand, as experts have revealed the tiny facial microexpressions that can give away a person's true thoughts.

This paper focuses on the research of cross-database micro-expression recognition, in which the training and test micro-expression samples belong to different microexpression databases. Mismatched feature distributions between the training and testing micro-expression feature degrade the performance of most well-performing micro-expression methods. To deal with cross-database micro-expression recognition, we propose a novel domain adaption method called Transfer Group Sparse Regression (TGSR). TGSR learns a sparse regression matrix for selecting salient facial local regions and the corresponding relationship of the training set and test set. We evaluate our TGSR model in CASME II and SMIC databases. Experimental results show that the proposed TGSR achieves satisfactory performance and outperforms most state-of-the-art subspace learning-based domain adaption methods.

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.

Facial expression recognition in videos is an active area of research in computer vision. However, fake facial expressions are difficult to be recognized even by humans. On the other hand, facial micro-expressions generally represent the actual emotion of a person, as it is a spontaneous reaction expressed through human face. Despite of a few attempts made for recognizing micro-expressions, still the problem is far from being a solved problem, which is depicted by the poor rate of accuracy shown by the state-of-the-art methods. A few CNN based approaches are found in the literature to recognize micro-facial expressions from still images. Whereas, a spontaneous micro-expression video contains multiple frames that have to be processed together to encode both spatial and temporal information. This paper proposes two 3D-CNN methods: MicroExpSTCNN and MicroExpFuseNet, for spontaneous facial micro-expression recognition by exploiting the spatiotemporal information in CNN framework. The MicroExpSTCNN considers the full spatial information, whereas the MicroExpFuseNet is based on the 3D-CNN feature fusion of the eyes and mouth regions. The experiments are performed over CAS(ME)^2 and SMIC micro-expression databases. The proposed MicroExpSTCNN model outperforms the state-of-the-art methods.

We present a system for covert automated deception detection using information available in a video. We study the importance of different modalities like vision, audio and text for this task. On the vision side, our system uses classifiers trained on low level video features which predict human micro-expressions. We show that predictions of high-level micro-expressions can be used as features for deception prediction. Surprisingly, IDT (Improved Dense Trajectory) features which have been widely used for action recognition, are also very good at predicting deception in videos. We fuse the score of classifiers trained on IDT features and high-level micro-expressions to improve performance. MFCC (Mel-frequency Cepstral Coefficients) features from the audio domain also provide a significant boost in performance, while information from transcripts is not very beneficial for our system. Using various classifiers, our automated system obtains an AUC of 0.877 (10-fold cross-validation) when evaluated on subjects which were not part of the training set. Even though state-of-the-art methods use human annotations of micro-expressions for deception detection, our fully automated approach outperforms them by 5%. When combined with human annotations of micro-expressions, our AUC improves to 0.922. We also present results of a user-study to analyze how well do average humans perform on this task, what modalities they use for deception detection and how they perform if only one modality is accessible.

Trying to handle and hide our true emotions is a challenge we all share as humans, and trying to discern what other people are hiding from us is something that fascinates us even more. Yet latest technological developments seem to signal that there is no place to hide anymore: Scientists at the University of Oulu in Finland have developed facial recognition software that can read human microexpressions at a success rate that beats humans at the same task. But what exactly are microexpressions? According to the Paul Ekman Group, founded by renowned psychologist Paul Ekman, who conducted groundbreaking research in the correlations between emotions and facial expressions – and has been dubbed "the best human lie detector in the world" in the process – microexpressions are "facial expressions that occur within 1/25th of a second and expose a person's true emotions". We make them involuntarily, even when we are trying to conceal our true emotional response. Perhaps more importantly, microexpressions are the same across all ages and genders, regardless of the person's unique cultural background, and they appear even when the subject is trying to hide their emotions without realizing they are doing so – in essence, when they are lying to themselves, too.

In the past two decades, we've seen chess grandmasters and the best Jeopardy players in the world alike fall in competition to computers. Heads-up No-limit Texas Hold'em poker may be next. But the future of artificial intelligence (AI) is about way more than games. Last April and May, Carnegie Mellon University's AI, Claudico (developed by Professor Tuomas Sandholm and his team), played an 80,000-hand tournament against four poker pros. When the game ended, three of the four players had bigger hands than Claudico.