Micro-expressions are brief facial expressions that last for 500 milliseconds or less and show the true emotional state of an individual when he or she is displaying a false emotional state. There are currently 2 different methods to train individuals to recognize micro-expressions-picture-based and video-based. Numerous organizations use micro-expression training as part of a deception detection program, but little research has been conducted on training outcomes, and no research has investigated the difference between the methods. In this quantitative study based on Darwin's theory of the universality of emotional expression, a control group experimental design was used to determine if there is a difference in training outcomes, as measured by post-training accuracy rates of overall and emotion-specific micro-expression identification, between the 2 current micro-expression training methods and no training. A total of 196 participants recruited from Amazon's Mechanical Turk community were randomly assigned to a picture-based training, video-based training, or no training control group. The online training and post-training test were delivered via a computer-based training platform. MANOVA, ANOVA and t-tests were run to determine the differences between the groups. Results indicated that participants in both picture-based and video-based training groups showed a significant increase in their ability to recognize micro-expressions compared to those in the no training group, but did not differ from each other. The study provides an increased understanding of micro-expression training outcomes that may contribute to the training of numerous law enforcement, security, and human resources professionals.

Les principales contributions de cette these, en analyse d'image, portent sur l’etude des caracteristiques de reperage et de reconnaissance des micro-expressions. les approches d’analyse d’expressions faciales dans le domaine de la vision par ordinateur consistent a les detecter et a les classer dans des videos. par rapport a la macro-expression, une micro-expression induit dans une partie du visage un changement rapide durant moins d'une demi-seconde. de plus, cette subtile apparition dans une partie du visage rend difficile sa detection et sa reconnaissance. ces dernieres annees ont connu un interet croissant pour des algorithmes d’extraction automatique de micro-expressions faciales. cela a ete motive par des applications dans des contextes a enjeux eleves tels les enquetes criminelles, les points de controle des aeroports et des transports en commun, le contre-terrorisme, … le choix de caracteristiques faciales efficaces joue un role crucial dans l’analyse des micro-expressions.ce travail se concentre sur la partie d’extraction de caracteristiques, en proposant diverses methodes pour les taches de detection et de reconnaissance de micro-expression.la detection constitue la premiere etape dans l'analyse des micro-expressions. les methodes de detection existantes basees sur des caracteristiques, tels les motifs binaires locaux (lbp), l’histogramme de gradients orientes (hog), le flux optique, souffrent de complexite de mise en œuvre entrainant un probleme d'implementation en temps reel. ainsi, dans cette these, une methode de detection basee sur la projection integrale est proposee pour resoudre ce probleme. cependant, toutes les caracteristiques ci-dessus sont extraites des visages recadres et rognees ; ce qui cause, generalement, un decalage residuel entre les images. pour resoudre ce probleme, est proposee une autre methode de detection basee sur des caracteristiques geometriques. cette derniere exploite les distances geometriques entre des points cles du visage sans necessite de recadrer l'image. ceci permet de capturer des deplacements geometriques subtils le long des sequences et s’avere approprie pour differentes taches d’analyse faciale qui requierent une grande vitesse de calcul.parmi les caracteristiques de reconnaissance de micro-expressions existantes, celles de mouvement basees sur le flux optique presentent des avantages dans la caracterisation de mouvements subtils sur le visage. toutefois, il reste difficile de determiner les emplacements precis de chaque mappage de traits du visage entre les differentes trames par flux optique, meme si les images ont ete alignees. un tel probleme peut donner lieu a une mauvaise estimation, a la fois, de l'orientation et de l’amplitude associees au flux optique. pour y pallier, nous proposons une nouvelle approche (dite fmbh) basee sur les histogrammes de frontiere de mouvement (mbh). elle permet de supprimer les mouvements inattendus causes par un mauvais recalage residuel apparaissant entre les images recadrees tout en capturant le mouvement relatif caracterisant la micro-expression. cette caracteristique est generee en combinant les composantes horizontales et verticales du differentiel de flux optique.les differents developpements de ce travail ont conduit a des etudes comparatives avec des approches de l'etat de l'art sur des bases de donnees bien connues et exploitees par la communaute du domaine. les resultats experimentaux, ainsi obtenues, montrent l'efficacite de nos contributions. / Recent years, there has been an increasing interest in the computer vision in automatic facial micro-expression algorithms. this has been driven by applications in high-stakes contexts such as criminal investigations, airport and mass transit checkpoints, counter terrorism, and so on. micro-expression approaches in computer vision area consist of detecting and classifying them from videos. compared to macro-expression, a micro-expression involves a rapid change which lasts less than a half of second, and moreover, its subtle appearance in part of the face makes detection and recognition difficult to achieve. effective facial features play a crucial role for micro-expression analysis. this thesis focuses on the feature extraction parts, by developing various feature extraction methods for types of micro-expression detection and recognition tasks.the detection of micro-expressions is the first step for its analysis. this thesis aims to spot micro-expressions from videos. existing detection methods based on features, such as the local binary patterns, the histogram of gradient, the optical flow suffer difficulties in computation consuming leading to real-time implementation problem. thus, in this thesis, the spotting method based on integral projection to address this problem. however, all the above features are extracted from cropped faces which usually cause residual mis-registration that appears between images. in order to deal with this issue, another detection method based on geometrical feature is proposed. it involves the geometrical distances between facial key-points without the need of cropping face. this captures subtle geometric displacements along sequences and is proved to be suitable for different facial analysis tasks that require high computational speed. for micro-expression recognition, motion features based on the optical flow have advantages in characterizing subtle movements on face among the existing recognition features. it is still a difficult problem for optical flow to determine the accurate locations of each facial feature mappings between different images even though the face images have been aligned. such an issue may give rise to wrong orientation and magnitude estimation associated to the optical flow field. in order to address this problem, the motion boundary histograms are considered. it can remove unexpected motions caused by residual mis-registration that appears between images cropped from different frames. nevertheless, the relative motion can be captured. based on the the motion boundary, a new descriptor the fusion motion boundary histograms is introduced. this feature is generated by combing both the horizontal and the vertical components of the differential of optical flow as inspired from the motion boundary histograms. the main contributions of this thesis lie at the study of features for micro-expressions spotting and recognition. experiments on the micro-expression databases show the effectiveness of the presented contributions.

Les micro-expressions sont des expressions faciales brèves et subtiles qui apparaissent et disparaissent en une fraction de seconde. Ce type d'expressions reflèterait "l'intention réelle" de l'être humain. Elles ont été étudiées pour mieux comprendre les communications non verbales et dans un contexte médicale lorsqu'il devient presque impossible d'engager une conversation ou d'essayer de traduire les émotions du visage ou le langage corporel d'un patient. Cependant, détecter et reconnaître les micro-expressions est une tâche difficile pour l'homme. Il peut donc être pertinent de développer des systèmes d'aide à la communication exploitant les micro-expressions. De nombreux travaux ont été réalisés dans les domaines de l'informatique affective et de la vision par ordinateur pour analyser les micro-expressions, mais une grande majorité de ces méthodes repose essentiellement sur des méthodes de vision par ordinateur classiques telles que les motifs binaires locaux, les histogrammes de gradients orientés et le flux optique. Étant donné que ce domaine de recherche est relativement nouveau, d'autres pistes restent à explorer. Dans cette thèse, nous présentons une nouvelle méthodologie pour l'analyse des petits mouvements (que nous appellerons par la suite mouvements subtils) et des micro-expressions. Nous proposons d'utiliser la pyramide de Riesz, une approximation multi-échelle et directionnelle de la transformation de Riesz qui a été utilisée pour l'amplification du mouvement dans les vidéos à l'aide de l'estimation de la phase 2D locale. Pour l'étape générale d'analyse de mouvements subtils, nous transformons une séquence d'images avec la pyramide de Riesz, extrayons et filtrons les variations de phase de l'image. Ces variations de phase sont en lien avec le mouvement. De plus, nous isolons les régions d'intérêt où des mouvements subtils pourraient avoir lieu en masquant les zones de bruit à l'aide de l'amplitude locale. La séquence d'image est transformée en un signal ID utilisé pour l'analyse temporelle et la détection de mouvement subtils. Nous avons créé notre propre base de données de séquences de mouvements subtils pour tester notre méthode. Pour l'étape de détection de micro-expressions, nous adaptons la méthode précédente au traitement de certaines régions d'intérêt du visage. Nous développons également une méthode heuristique pour détecter les micro-événements faciaux qui sépare les micro-expressions réelles des clignotements et des mouvements subtils des yeux. Pour la classification des micro-expressions, nous exploitons l'invariance, sur de courtes durées, de l'orientation dominante issue de la transformation de Riesz afin de moyenner la séquence d'une micro-expression en une paire d'images. A partir de ces images, nous définissons le descripteur MORF (Mean Oriented Riesz Feature) constitué d'histogrammes d'orientation. Les performances de nos méthodes sont évaluées à l'aide de deux bases de données de micro-expressions spontanées. / Micro-expressions are brief and subtle facial expressions that go on and off the face in a fraction of a second. This kind of facial expressions usually occurs in high stake situations and is considered to reflect a humans real intent. They have been studied to better understand non-verbal communications and in medical applications where is almost impossible to engage in a conversation or try to read the facial emotions or body language of a patient. There has been some interest works in micro-expression analysis, however, a great majority of these methods are based on classically established computer vision methods such as local binary patterns, histogram of gradients and optical flow. Considering the fact that this area of research is relatively new, much contributions remains to be made. ln this thesis, we present a novel methodology for subtle motion and micro-expression analysis. We propose to use the Riesz pyramid, a multi-scale steerable Hilbert transformer which has been used for 2-D phase representation and video amplification, as the basis for our methodology. For the general subtle motion analysis step, we transform an image sequence with the Riesz pyramid, extract and lifter the image phase variations as proxies for motion. Furthermore, we isolate regions of intcrcst where subtle motion might take place and mask noisy areas by thresholding the local amplitude. The total sequence is transformed into a ID signal which is used fo temporal analysis and subtle motion spotting. We create our own database of subtle motion sequences to test our method. For the micro-expression spotting step, we adapt the previous method to process some facial regions of interest. We also develop a heuristic method to detect facial micro-events that separates real micro-expressions from eye blinkings and subtle eye movements. For the micro-expression classification step, we exploit the dominant orientation constancy fom the Riesz transform to average the micro-expression sequence into an image pair. Based on that, we introduce the Mean Oriented Riesz Feature descriptor. The accuracy of our methods are tested in Iwo spontaneous micro-expressions databases. Furthermore, wc analyse the parameter variations and their effect in our results.

<div> <p>The purpose of this research was to examine the perception of micro-expressions in animated characters with different visual styles. Specifically, the work reported in this thesis sought to examine: (1) whether people can recognize micro-expressions in animated characters, (2) whether there are differences in recognition based on the character visual style (stylized versus realistic), (3) the extent to which the degree of exaggeration of micro-expressions affect the perceived naturalness and intensity of the animated characters’ emotion, and (4) whether there are differences in effects on perceived naturalness and intensity based on the character visual style. The research work involved two experiments: a recognition study and an emotion rating study. A total of 275 participants participated in both experiments. In the recognition study, the participants watched eight micro-expression animations representing four different emotions (happy, sad, fear, surprised). Four animations featured a stylized character and four a realistic character. For each animation, subjects were asked to identify the character’s emotion conveyed by the micro-expression. Results showed that all four emotions for both characters were recognized with an acceptable degree of accuracy. The recognition rates of the stylized character were 84.73% for happiness, 88.73% for sadness, 60.73% for fear, and 83.64% surprise. The recognition rates of the realistic character were 87.37% for happiness, 82.94% for sadness, 69.62% for fear, and 77.13% for surprise. In the emotion rating study, participants watched two sets of eight animation clips (16 clips in total). Eight animations in each set featured the character performing both macro- and micro-expressions, the different between these two sets was the exaggeration degree of micro-expressions (normal vs exaggerated). Participants were asked to recognize the character’s true emotion (conveyed by the micro-expressions) and rate the naturalness and intensity of the character’s emotion in each clip using a 5-point Likert scale. Findings showed that the <b>degree of exaggeration of the micro-expressions </b>had a significant effect on <b>emotion’s</b> <b>naturalness rating</b>, <b>emotion’s</b> <b>intensity rating</b>, and <b>true emotion recognition</b> and the <b>character visual style</b> had a significant effect on emotion’s <b>intensity rating</b>. Emotion type, participant gender and participant animation experience also had significant effects on perception of the micro-expression.</p> </div> <br>

Lie-detection has been an evergreen and evolving subject. Polygraph techniques have been the most popular and successful technique till date. The main drawback of the polygraph is that good results cannot be attained without maintaining a physical contact, of the subject under test. In general, this physical contact would induce extra consciousness in the subject. Also, any sort of arousal in the subject triggers false positives while performing the traditional polygraph based tests. With all these drawbacks in the polygraph, also, due to rapid developments in the fields of computer vision and artificial intelligence, with newer and faster algorithms, have compelled mankind to search and adapt to contemporary methods in lie-detection. Observing the facial expressions of emotions in a person without any physical contact and implementing these techniques using artificial intelligence is one such method. The concept of magnifying a micro expression and trying to decipher them is rather premature at this stage but would evolve in future. Magnification using EVM technique has been proposed recently and it is rather new to extract these micro expressions from magnified EVM based on HOG features. Till date, HOG features have been used in conjunction with SVM, and generally for person/pedestrian detection. A newer, simpler and contemporary method of applying EVM with HOG features and Back-propagation Neural Network jointly has been introduced and proposed to extract and decipher the micro-expressions on the face. Micro-expressions go unnoticed due to its involuntary nature, but EVM is used to magnify them and makes them noticeable. Emotions behind the micro-expressions are extracted and recognized using the HOG features \&amp; Back-Propagation Neural Network. One of the important aspects that has to be dealt with human beings is a biased mind. Since, an investigator is also a human and, he too, has to deal with his own assumptions and emotions, a Neural Network is used to give the investigator an unbiased start in identifying the true emotions behind every micro-expression. On the whole, this proposed system is not a lie-detector, but helps in detecting the emotions of the subject under test. By further investigation, a lie can be detected. / This thesis uses a magnification technique to magnify the subtle, faint and spontaneous facial muscle movements or more precisely, micro-expressions. This magnification would help a system in classifying them and estimating the emotion behind them. This technique additionally magnifies the color changes, which could be used to extract the pulse without a physical contact with the subject. The results are presented in a GUI. / Gautam: +46(0)739528573, +91-9701534064 Tushal: +46(0)723219833, +91-9000242241 Venu: +46(0)734780266, +91-9298653191 Sai: +91-9989410111