Emotion recognition of static and dynamic faces in autism spectrum disorder

There is substantial evidence for facial emotion recognition (FER) deficits in autism spectrum disorder (ASD). The extent of this impairment, however, remains unclear, and there is some suggestion that clinical groups might benefit from the use of dynamic rather than static images. High-functioning individuals with ASD (n = 36) and typically developing controls (n = 36) completed a computerised FER task involving static and dynamic expressions of the six basic emotions. The ASD group showed poorer overall performance in identifying anger and disgust and were disadvantaged by dynamic (relative to static) stimuli when presented with sad expressions. Among both groups, however, dynamic stimuli appeared to improve recognition of anger. This research provides further evidence of specific impairment in the recognition of negative emotions in ASD, but argues against any broad advantages associated with the use of dynamic displays.

Functional MRI of facial emotion recognition deficits in schizophrenia and their electrophysiological correlates

Empirical evidence suggests impaired facial emotion recognition in schizophrenia. However, the nature of this deficit is the subject of ongoing research. The current study tested the hypothesis that a generalized deficit at an early stage of face-specific processing (i.e. putatively subserved by the fusiform gyrus) accounts for impaired facial emotion recognition in schizophrenia as opposed to the Negative Emotion-specific Deficit Model, which suggests impaired facial information processing at subsequent stages. Event-related potentials (ERPs) were recorded from 11 schizophrenia patients and 15 matched controls while performing a gender discrimination and a facial emotion recognition task. Significant reduction of the face-specific vertex positive potential (VPP) at a peak latency of 165 ms was confirmed in schizophrenia subjects whereas their early visual processing, as indexed by P1, was found to be intact. Attenuated VPP was found to correlate with subsequent P3 amplitude reduction and to predict accuracy when performing a facial emotion discrimination task. A subset of ten schizophrenia patients and ten matched healthy control subjects also performed similar tasks in the magnetic resonance imaging scanner. Patients showed reduced blood oxygenation level-dependent (BOLD) activation in the fusiform, inferior frontal, middle temporal and middle occipital gyrus as well as in the amygdala. Correlation analyses revealed that VPP and the subsequent P3a ERP components predict fusiform gyrus BOLD activation. These results suggest that problems in facial affect recognition in schizophrenia may represent flow-on effects of a generalized deficit in early visual processing.

A generalised deficit can account for problems in facial emotion recognition in schizophrenia

Neuroimaging research has shown localised brain activation to different facial expressions. This, along with the finding that schizophrenia patients perform poorly in their recognition of negative emotions, has raised the suggestion that patients display an emotion specific impairment. We propose that this asymmetry in performance reflects task difficulty gradations, rather than aberrant processing in neural pathways subserving recognition of specific emotions. A neural network model is presented, which classifies facial expressions on the basis of measurements derived from human faces. After training, the network showed an accuracy pattern closely resembling that of healthy subjects. Lesioning of the network led to an overall decrease in the network’s discriminant capacity, with the greatest accuracy decrease to fear, disgust and anger stimuli. This implies that the differential pattern of impairment in schizophrenia patients can be explained without having to postulate impairment of specific processing modules for negative emotion recognition.

Inpatient facial expression recognition with SMS alert

Facial expression recognition (FER) has been dramatically developed in recent years, thanks to the advancements in related fields, especially machine learning, image processing and human recognition. Accordingly, the impact and potential usage of automatic FER have been growing in a wide range of applications, including human-computer interaction, robot control and driver state surveillance. However, to date, robust recognition of facial expressions from images and videos is still a challenging task due to the difficulty in accurately extracting the useful emotional features. These features are often represented in different forms, such as static, dynamic, point-based geometric or region-based appearance. Facial movement features, which include feature position and shape changes, are generally caused by the movements of facial elements and muscles during the course of emotional expression. The facial elements, especially key elements, will constantly change their positions when subjects are expressing emotions. As a consequence, the same feature in different images usually has different positions. In some cases, the shape of the feature may also be distorted due to the subtle facial muscle movements. Therefore, for any feature representing a certain emotion, the geometric-based position and appearance-based shape normally changes from one image to another image in image databases, as well as in videos. This kind of movement features represents a rich pool of both static and dynamic characteristics of expressions, which playa critical role for FER. The vast majority of the past work on FER does not take the dynamics of facial expressions into account. Some efforts have been made on capturing and utilizing facial movement features, and almost all of them are static based. These efforts try to adopt either geometric features of the tracked facial points, or appearance difference between holistic facial regions in consequent frames or texture and motion changes in loca- facial regions. Although achieved promising results, these approaches often require accurate location and tracking of facial points, which remains problematic.

Mirror neuron activation is associated with facial emotion processing

Theoretical accounts suggest that mirror neurons play a crucial role in social cognition. The current study used transcranial-magnetic stimulation (TMS) to investigate the association between mirror neuron activation and facialemotion processing, a fundamental aspect of social cognition, among healthy adults (n = 20). Facial emotion processing of static (but not dynamic) images correlated significantly with an enhanced motor response, proposed to reflect mirror neuron activation. These correlations did not appear to reflect general facial processing or pattern recognition, and provide support to current theoretical accounts linking the mirror neuron system to aspects of social cognition. We discuss the mechanism by which mirror neurons might facilitate facial emotion recognition.

Facial emotion processing in schizophrenia : no evidence for a deficit specific to negative emotions in a differential deficit design

People with schizophrenia perform poorly when recognising facial expressions of emotion, particularly negative emotions such as fear. This finding has been taken as evidence of a “negative emotion specific deficit”, putatively associated with a dysfunction in the limbic system, particularly the amygdala. An alternative explanation is that greater difficulty in recognising negative emotions may reflect a priori differences in task difficulty. The present study uses a differential deficit design to test the above argument. Facial emotion recognition accuracy for seven emotion categories was compared across three groups. Eighteen schizophrenia patients and one group of healthy age- and gender-matched controls viewed identical sets of stimuli. A second group of 18 age- and gender-matched controls viewed a degraded version of the same stimuli. The level of stimulus degradation was chosen so as to equate overall level of accuracy to the schizophrenia patients. Both the schizophrenia group and the degraded image control group showed reduced overall recognition accuracy and reduced recognition accuracy for fearful and sad facial stimuli compared with the intact-image control group. There were no differences in recognition accuracy for any emotion category between the schizophrenia group and the degraded image control group. These findings argue against a negative emotion specific deficit in schizophrenia.

Social cars : sensing, gathering, sharing and conveying social cues to road users

Intelligent Transport Systems (ITS) resembles the infrastructure for ubiquitous computing in the car. It encompasses a) all kinds of sensing technologies within vehicles as well as road infrastructure, b) wireless communication protocols for the sensed information to be exchanged between vehicles (V2V) and between vehicles and infrastructure (V2I), and c) appropriate intelligent algorithms and computational technologies that process these real-time streams of information. As such, ITS can be considered a game changer. It provides the fundamental basis of new, innovative concepts and applications, similar to the Internet itself. The information sensed or gathered within or around the vehicle has led to a variety of context-aware in-vehicular technologies within the car. A simple example is the Anti-lock Breaking System (ABS), which releases the breaks when sensors detect that the wheels are locked. We refer to this type of context awareness as vehicle/technology awareness. V2V and V2I communication, often summarized as V2X, enables the exchange and sharing of sensed information amongst cars. As a result, the vehicle/technology awareness horizon of each individual car is expanded beyond its observable surrounding, paving the way to technologically enhance such already advanced systems. In this chapter, we draw attention to those application areas of sensing and V2X technologies, where the human (driver), the human’s behavior and hence the psychological perspective plays a more pivotal role. The focal points of our project are illustrated in Figure 1: In all areas, the vehicle first (1) gathers or senses information about the driver. Rather than to limit the use of such information towards vehicle/technology awareness, we see great potential for applications in which this sensed information is then (2) fed back to the driver for an increased self-awareness. In addition, by using V2V technologies, it can also be (3) passed to surrounding drivers for an increased social awareness, or (4), pushed even further, into the cloud, where it is collected and visualized for an increased, collective urban awareness within the urban community at large, which includes all city dwellers.

Service encounter needs theory : a dyadic, psychosocial approach to understanding service encounters

Interactions between customers and service providers are ubiquitous. Some of these encounters are routine, but many are characterized by conflict and intense emotions. This chapter introduces a new theory, service encounter needs theory (SENT) that aims to elucidate the mechanisms through which service encounter behaviors affect outcomes for customers and employees. Evidence is presented for the preeminence within these encounters of eight psychosocial needs, and propositions are advanced regarding likely antecedents to fulfillment and violation of these needs. Emotional experiences and displays are viewed as important consequences of need fulfillment and violation, as are numerous cognitive, behavioral, and health-related outcomes.

Symptom correlates of static and dynamic facial affect processing in schizophrenia : evidence of a double dissociation?

Schizophrenia patients have been shown to be compromised in their ability to recognize facial emotion. This deficit has been shown to be related to negative symptoms severity. However, to date, most studies have used static rather than dynamic depictions of faces. Nineteen patients with schizophrenia were compared with seventeen controls on 2 tasks; the first involving the discrimination of facial identity, emotion, and butterfly wings; the second testing emotion recognition using both static and dynamic stimuli. In the first task, the patients performed more poorly than controls for emotion discrimination only, confirming a specific deficit in facial emotion recognition. In the second task, patients performed more poorly in both static and dynamic facial emotion processing. An interesting pattern of associations suggestive of a possible double dissociation emerged in relation to correlations with symptom ratings: high negative symptom ratings were associated with poorer recognition of static displays of emotion, whereas high positive symptom ratings were associated with poorer recognition of dynamic displays of emotion. However, while the strength of associations between negative symptom ratings and accuracy during static and dynamic facial emotion processing was significantly different, those between positive symptom ratings and task performance were not. The results confirm a facial emotion-processing deficit in schizophrenia using more ecologically valid dynamic expressions of emotion. The pattern of findings may reflect differential patterns of cortical dysfunction associated with negative and positive symptoms of schizophrenia in the context of differential neural mechanisms for the processing of static and dynamic displays of facial emotion.