Growing evidence supports the effectiveness of cognitive behavioural therapy (CBT) for psychosis, including CBT for voices (CBTv), which targets auditory verbal hallucinations (AVH). CBT may be a promising approach for improving information processing difficulties in schizophrenia, and by so doing, facilitating social cognition and daily functioning. While many studies have tested treatment effects in schizophrenia, none have specifically evaluated electrophysiological changes in brain activity following CBT in patients with schizophrenia. Electrophysiological studies have revealed a number of event related potentials (ERPs) associated with impaired processing of emotional facial expressions in patients with schizophrenia. This well-documented difficulty with facial expression recognition has been associated with impaired low-level visual information processing. However, there is only limited and inconsistent data on the way in which early visual processing deficits are related to impaired emotional expression processing in this patient population. The research presented in this thesis assessed changes in ERPs to emotional expressions following cognitive behavioural therapy for voices (CBTv) in patients with schizophrenia who experience auditory hallucinations. The studies presented also examined ERPs evoked in response to spatial frequency filtered (SF-filtered) and unfiltered images of facial expressions and control objects in healthy controls and a homogenous sample of schizophrenia patients – those experiencing auditory verbal hallucinations. This was done to test certain hypotheses regarding the low-level genesis of face recognition difficulties in schizophrenia. Relative to controls, patients with schizophrenia indicated blunted: 1) early-stage visual information processing to sad, angry and fearful facial expressions (as indexed by the amplitude of the P100 ERP), 2) facial structural encoding to neutral, joyful, sad, angry and fearful facial expression (as indexed by the N170), and 3) higher-order decoding of all facial expressions (indexed by mean amplitude of the P300). Assessment of SF-filtered facial expressions found impaired early processing (i.e., P100) specific to low spatial frequency (LSF) filtered fearful facial expression and high spatial frequency (HSF) filtered neutral faces in patients with schizophrenia, which at later stages (i.e., N170 and P300) extended to all facial expressions and SF filtering conditions. Within-group comparisons showed that patients exhibited a different pattern of ERP modulation across facial expressions than controls for P100 and N170, but not for P300. The within-group comparisons also suggested a heightened response to LSF threatening information, relative to BSF conditions, in the patient group. CBTv therapy did not change ERP amplitudes in response to facial expressions, but was associated with decreased latency in the P100. This improved processing speed was not reflected in later ERP components (i.e., N170 and P300). These results indicate that earlier perceptual processing impairments are expression-specific and that behavioural and electrophysiological face-processing deficits in schizophrenia arise from early-stage deficits in visual processing. The finding of an improvement in visual processing speed to facial expressions following CBTv treatment provides the first demonstration of CBTv-induced changes to brain responses to facial expressions at an early neural processing stage.

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Face processing deficits appear early in autism and greatly impact subsequent development. In this paper, the N170 component, an event-related brain potential sensitive to face processing, is examined in children with autism and typical development. The N170 amplitude was sensitive to group differences, as children with typical development showed greater differentiation to upright vs. inverted stimuli and faces vs. vehicles than children with autism. The N170 was also delayed in children with autism. The N170 was not a sensitive marker of individual differences in social behavior and autistic symptomology, but the proceeding positive peak, the P1, was a sensitive marker of individual differences in children with typical development. Results suggest that children with autism and children with typical development employ different face processing strategies, even for the basic encoding of a face.

This present study investigated whether face-type (stereotypical or nonstereotypical) facilitates stereotype-consistent categorization and decision-making. Previous literature regarding adults has suggested an associative link between stereotypically Black facial features and assumed criminality. This study seeks to extend these findings by investigating whether the same heuristic processes that underpin biased decisions regarding adult phenotypic racially stereotypical features (e.g., broad nose, full lips) extend to children’s faces. That is, do the negative stereotypes (i.e., criminal Black male) that influence face-type judgments in adults extend to child face-type judgements as well. In two studies testing face-type categorization and disciplinary judgments, people were more likely to miscategorize children with stereotypical faces into negative roles more than positive roles. People were also more likely to increase their disciplinary judgments from one infraction to another for children with stererotypical faces compared to atypical faces. Results suggest that face-type cues do extend to children and also engender negative associations.

Yes / The application of computer vision in face processing remains an important research field. The aim of this chapter is to provide an up-to-date review of research efforts of computer vision scientist in facial image processing, especially in the areas of entertainment industry, surveillance, and other human computer interaction applications. To be more specific, this chapter reviews and demonstrates the techniques of visible facial analysis, regardless of specific application areas. First, the chapter makes a thorough survey and comparison of face detection techniques. It provides some demonstrations on the effect of computer vision algorithms and colour segmentation on face images. Then, it reviews the facial expression recognition from the psychological aspect (Facial Action Coding System, FACS) and from the computer animation aspect (MPEG-4 Standard). The chapter also discusses two popular existing facial feature detection techniques: Gabor feature based boosted classifiers and Active Appearance Models, and demonstrate the performance on our in-house dataset. Finally, the chapter concludes with the future challenges and future research direction of facial image processing. © 2011, IGI Global.

No / The application of computer vision in face processing remains an important research field. The aim of this chapter is to provide an up-to-date review of research efforts of computer vision scientist in facial image processing, especially in the areas of entertainment industry, surveillance, and other human computer interaction applications. To be more specific, this chapter reviews and demonstrates the techniques of visible facial analysis, regardless of specific application areas. First, the chapter makes a thorough survey and comparison of face detection techniques. It provides some demonstrations on the effect of computer vision algorithms and colour segmentation on face images. Then, it reviews the facial expression recognition from the psychological aspect (Facial Action Coding System, FACS) and from the computer animation aspect (MPEG-4 Standard). The chapter also discusses two popular existing facial feature detection techniques: Gabor feature based boosted classifiers and Active Appearance Models, and demonstrate the performance on our in-house dataset. Finally, the chapter concludes with the future challenges and future research direction of facial image processing.

In the last twenty years, important advances have been made in the field of automatic face processing, given the importance of human faces for personal identification, emotional expression and verbal and non verbal communication. The very first step in a face processing algorithm is the detection of faces; while this is a trivial problem in controlled environments, the detection of faces in real environments is still a challenging task. Until now, the most successful approaches for face detection represent the face as a grey-level pattern, and the problem itself is considered as the classification between "face" and "non-face" patterns. Satisfactory results have been achieved in this area. The main disadvantage is that an exhaustive search has to be done on each image in order to locate the faces. This search normally involves testing every single position on the image at different scales, and although this does not represent an important drawback in off-line face processing systems, in those cases where a real-time response is needed it is still a problem. In the different proposed methods for face detection, the "observer" is a disembodied entity, which holds no relationship with the observed scene. This thesis presents a framework for an efficient location of faces in real scenes, in which, by considering both the observer to be situated in the world, and the relationships that hold between the two, a set of constraints in the search space can be defined. The constraints rely on two main assumptions; first, the observer can purposively interact with the world (i.e. change its position relative to the observed scene) and second, the camera is fully calibrated. The first source constraint is the structural information about the observer environment, represented as a depth map of the scene in front of the camera. From this representation the search space can be constrained in terms of the range of scales where a face might be found as different positions in the image. The second source of constraint is the geometrical relationship between the camera and the scene, which allows us to project a model of the subject into the scene in order to eliminate those areas where faces are unlikely to be found. In order to test the proposed framework, a system based on the premises stated above was constructed. It is based on three different modules: a face/non-face classifier, a depth estimation module and a search module. The classifier is composed of a set of convolutional neural networks (CNN) that were trained to differentiate between face and non-face patterns, the depth estimation modules uses a multilevel algorithm to compute the scene depth map from a sequence of images captured the depth information and the subject model into the image where the search will be performed in order to constrain the search space. Finally, the proposed system was validated by running a set of experiments on the individual modules and then on the whole system.

Cognitive impairment is a key characteristic of schizophrenia and is a clear predictor of functional outcome. This thesis explores the relationship between cognitive ability relating to social and non-social processing. Schizophrenia patients demonstrate an impaired ability to recognise, label and discriminate emotional expression within the face. The underlying mechanisms behind this social cognitive impairment are not yet fully understood. This thesis explores the notion that a basic perceptual impairment in processing facial information adversely impacts on the perception of more complex information derived from faces, such as emotional expression. Face perception relies on processing the featural characteristics of a face as well as the relationship between these features. Information pertaining to the spatial distances between features is referred to as configural information. / A group of schizophrenia patients and healthy control participants completed a battery of tasks that assessed basic neurocognition, facial emotion processing and configural face processing. A model of face processing was proposed and used to systematically pinpoint specific deficits that may contribute to impaired face processing in schizophrenia. The results indicated that schizophrenia patients show impairments on three broad constructs; basic neurocognition, facial emotion processing, and most pertinently, deficits in configural processing. It was revealed that although neurocognitive and face processing both explained a significant proportion of the variance in facial emotion processing, the effect of neurocognition was indirect and mediated by face processing. / To investigate the diagnostic specificity of these findings, a group of bipolar disorder patients was also tested on the task battery. The results indicated that bipolar disorder patients also show social and non-social cognitive impairments, however, not as severe as that demonstrated by the schizophrenia patients. Furthermore, the effect of neurocognitive performance on facial emotion processing appeared more direct for bipolar disorder patients compared to schizophrenia patients. Although deficits in face processing were observable in bipolar, they were not specific to configural processing. Thus, deficits in emotion processing were more associated to neurocognitive ability in bipolar disorder patients, and more associated to configural face processing in schizophrenia patients. The configural processing deficits in schizophrenia are discussed as a lower-order perception problem. In conclusion, the results of this thesis are discussed in terms of their implication for treatment.

Schizophrenia is a psychiatric disorder characterized by social dysfunction. People with schizophrenia misinterpret social information and it is suggested that this difficulty may result from visual processing deficits. As faces are one of the most important sources of social information it is hypothesized that people suffering from the disorder have impairments in the visual face processing system. It is unclear which mechanism of the face processing system is impaired but two types of deficits are most often proposed: a deficit in face perception in general (i.e., processing of facial features as such) and a deficit in facial emotion processing (i.e., recognition of emotional facial expressions). Due to the contradictory evidence from behavioural, electrophysiological as well as neuroimaging studies offering support for the involvement of one or the other deficit in schizophrenia it is early to make any conclusive statements as to the nature and level of impairment. Further studies are needed for a better understanding of the key mechanism and abnormalities underlying social dysfunction in schizophrenia.