Intelligent facial emotion recognition using a layered encoding cascade optimization model

In this research, we propose a facial expression recognition system with a layered encoding cascade optimization model. Since generating an effective facial representation is a vital step to the success of facial emotion recognition, a modified Local Gabor Binary Pattern operator is first employed to derive a refined initial face representation and we then propose two evolutionary algorithms for feature optimization including (i) direct similarity and (ii) Pareto-based feature selection, under the layered cascade model. The direct similarity feature selection considers characteristics within the same emotion category that give the minimum within-class variation while the Pareto-based feature optimization focuses on features that best represent each expression category and at the same time provide the most distinctions to other expressions. Both a neural network and an ensemble classifier with weighted majority vote are implemented for the recognition of seven expressions based on the selected optimized features. The ensemble model also automatically updates itself with the most recent concepts in the data. Evaluated with the Cohn-Kanade database, our system achieves the best accuracies when the ensemble classifier is applied, and outperforms other research reported in the literature with 96.8% for direct similarity based optimization and 97.4% for the Pareto-based feature selection. Cross-database evaluation with frontal images from the MMI database has also been conducted to further prove system efficiency where it achieves 97.5% for Pareto-based approach and 90.7% for direct similarity-based feature selection and outperforms related research for MMI. When evaluated with 90° side-view images extracted from the videos of the MMI database, the system achieves superior performances with >80% accuracies for both optimization algorithms. Experiments with other weighting and meta-learning combination methods for the construction of ensembles are also explored with our proposed ensemble showing great adpativity to new test data stream for cross-database evaluation. In future work, we aim to incorporate other filtering techniques and evolutionary algorithms into the optimization models to further enhance the recognition performance.

Autism blogs: expressed emotion, language styles and concerns in personal and community settings

The Internet has provided an ever increasingly popular platform for individuals to voice their thoughts, and like-minded people to share stories. This unintentionally leaves characteristics of individuals and communities, which are often difficult to be collected in traditional studies. Individuals with autism are such a case, in which the Internet could facilitate even more communication given its social-spatial distance being a characteristic preference for individuals with autism. Previous studies examined the traces left in the posts of online autism communities (Autism) in comparison with other online communities (Control). This work further investigates these online populations through the contents of not only their posts but also their comments. We first compare the Autism and Control blogs based on three features: topics, language styles and affective information. The autism groups are then further examined, based on the same three features, by looking at their personal (Personal) and community (Community) blogs separately. Machine learning and statistical methods are used to discriminate blog contents in both cases. All three features are found to be significantly different between Autism and Control, and between autism Personal and Community. These features also show good indicative power in prediction of autism blogs in both personal and community settings.

Emotion processing fails to modulate putative mirror neuron response to trained visuomotor associations

Recent neuroimaging studies have demonstrated that activation of the putative human mirror neuron system (MNS) can be elicited via visuomotor training. This is generally interpreted as supporting an associative learning account of the mirror neuron system (MNS) that argues against the ontogeny of the MNS to be an evolutionary adaptation for social cognition. The current study assessed whether a central component of social cognition, emotion processing, would influence the MNS activity to trained visuomotor associations, which could support a broader role of the MNS in social cognition. Using functional magnetic resonance imaging (fMRI), we assessed repetition suppression to the presentation of stimulus pairs involving a simple hand action and a geometric shape that was either congruent or incongruent with earlier association training. Each pair was preceded by an image of positive, negative, or neutral emotionality. In support of an associative learning account of the MNS, repetition suppression was greater for trained pairs compared with untrained pairs in several regions, primarily supplementary motor area (SMA) and right inferior frontal gyrus (rIFG). This response, however, was not modulated by the valence of the emotional images. These findings argue against a fundamental role of emotion processing in the mirror neuron response, and are inconsistent with theoretical accounts linking mirror neurons to social cognition.

Sexual Risk Recognition Deficits: The Role of Prior Victimization and Emotion Dysregulation

Recent theoretical writings suggest that the ineffective regulation of negative emotional states may reduce the ability of women to detect and respond effectively to situational and interpersonal factors that increase risk for sexual assault. However, little empirical research has explored this hypothesis. In the present study, it was hypothesized that prior sexual victimization and negative mood state would each independently predict poor risk recognition and less effective defensive actions in response to an analogue sexual assault vignette. Further, these variables were expected to interact to produce particularly impaired risk responses. Finally, that the in vivo emotion regulation strategy of suppression and corresponding cognitive resource usage (operationalized as memory impairment for the vignette) were hypothesized to mediate these associations. Participants were 668 female undergraduate students who were randomly assigned to receive a negative or neutral film mood induction followed by an audiotaped dating interaction during which they were instructed to indicate when the man had “gone too far” and describe an adaptive response to the situation. Approximately 33.5% of the sample reported a single victimization and 10% reported revictimization. Hypotheses were largely unsupported as sexual victimization history, mood condition, and their interaction did not impact risk recognition or adaptive responding. However, in vivo emotional suppression and cognitive resource usage were shown to predict delayed risk recognition only. Findings suggest that contrary to hypotheses, negative mood (as induced here) may not relate to risk recognition and response impairments. However, it may be important for victimization prevention programs that focus on risk perception to address possible underlying issues with emotional suppression and limited cognitive resources to improve risk perception abilities. Limitations and future directions are discussed.

COMT Met (158) modulates facial emotion recognition in bipolar I disorder mood episodes

Background: One of the many cognitive deficits reported in bipolar disorder (BD) patients is facial emotion recognition (FER), which has recently been associated with dopaminergic catabolism. Catechol-O-methyltransferase (COMT) is one of the main enzymes involved in the metabolic degradation of dopamine (DA) in the prefrontal cortex (PFC). The COMT gene polymorphism rs4680 (Val(158)Met) Met allele is associated with decreased activity of this enzyme in healthy controls. The objective of this study was to evaluate the influence of Val(158)Met on FER during manic and depressive episodes in BD patients and in healthy controls. Materials and methods: 64 BD type I patients (39 in manic and 25 in depressive episodes) and 75 healthy controls were genotyped for COMT rs4680 and assessed for FER using the Ekman 60 Faces (EK60) and Emotion Hexagon (Hx) tests. Results: Bipolar manic patients carrying the Met allele recognized fewer surprised faces, while depressed patients with the Met allele recognized fewer "angry" and "happy" faces. Healthy homozygous subjects with the Met allele had higher FER scores on the Hx total score, as well as on "disgust" and "angry" faces than other genotypes. Conclusion: This is the first study suggesting that COMT rs4680 modulates FER differently during BD episodes and in healthy controls. This provides evidence that PFC DA is part of the neurobiological mechanisms of social cognition. Further studies on other COMT polymorphisms that include euthymic BD patients are warranted. ClinicalTrials.gov Identifier: NCT00969. (C) 2011 Elsevier B.V. All rights reserved.

Perception of emotion in sounded and imagined music

WE STUDIED THE EMOTIONAL RESPONSES BY MUSICIANS to familiar classical music excerpts both when the music was sounded, and when it was imagined.We used continuous response methodology to record response profiles for the dimensions of valence and arousal simultaneously and then on the single dimension of emotionality. The response profiles were compared using cross-correlation analysis, and an analysis of responses to musical feature turning points, which isolate instances of change in musical features thought to influence valence and arousal responses. We found strong similarity between the use of an emotionality arousal scale across the stimuli, regardless of condition (imagined or sounded). A majority of participants were able to create emotional response profiles while imagining the music, which were similar in timing to the response profiles created while listening to the sounded music.We conclude that similar mechanisms may be involved in the processing of emotion in music when the music is sounded and when imagined.

Maladaptive Emotion Regulation is Related to Distressed Personalities in Cardiac Patients

Cardiac patients with Type D ('distressed') personality perceive more stress. It is unclear to what extent Type D personality might represent deficits in emotion regulation that are known to play an important role in the development of mental disorders. This study evaluated the relationship between emotion regulation and Type D personality and assessed the influence of mood and stress on Type D.