The Neuroscience of Face Processing and Identification in Eyewitnesses and Offenders

Werner, Nicole-Simone; Kühnel, Sina; Markowitsch, Hans J.

doi:10.3389/fnbeh.2013.00189

Cited by 12 publications

(7 citation statements)

References 162 publications

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“…In emotion research anger is for instance operationalized in brain response differences during the perception of violent versus neutral images 11 . Another commonly used approach is assessing brain response differences during the perception of facial expressions contrasting neutral expressions with emotional ones, such as angry 12 , happy, fearful and sad 13 . Moreover, avoidance-approach paradigms have been utilized measuring the automatic action tendency responses to facial expressions while participants were instructed to avoid angry and approach happy faces 14 .…”

Section: Introductionmentioning

confidence: 99%

Anger provocation in violent offenders leads to emotion dysregulation

Tonnaer

Siep

Zutphen

et al. 2017

Sci Rep

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Anger and anger regulation problems that result in aggressive behaviour pose a serious problem for society. In this study we investigated differences in brain responses during anger provocation or anger engagement, as well as anger regulation or distraction from anger, and compared 16 male violent offenders to 18 non-offender controls. During an fMRI adapted provocation and regulation task participants were presented with angry, happy and neutral scenarios. Prior research on violent offenders indicates that a combination of increased limbic activity (involved in emotion), along with decreased prefrontal activity (involved in emotion regulation), is associated with reactive aggression. We found increased ventrolateral prefrontal activity during anger engagement in violent offenders, while decreased dorsolateral and ventrolateral prefrontal activity was found during anger distraction. This activity pattern was specific for anger. We found no exclusive pattern for happiness. In violent offenders, this suggests an increased need to regulate specifically during anger engagement and regulation difficulties when explicitly instructed to distract. The constant effort required for violent offenders to regulate anger might exhaust the necessary cognitive resources, resulting in a risk for self-control failure. Consequently, continuous provocation might ultimately contribute to reactive aggression.

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Section: Introductionmentioning

confidence: 99%

Anger provocation in violent offenders leads to emotion dysregulation

Tonnaer

Siep

Zutphen

et al. 2017

Sci Rep

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“…Several category-selective areas are found in the lateral and ventral visual cortex, such as the face-selective areas (fusiform face area, FFA, and occipital face area, OFA) [ 6 – 8 ] and the house-selective areas (parahippocampal place area, PPA, and transverse occipital sulcus, TOS) [ 9 , 10 ]. More recently, several studies have demonstrated that these category-selective areas respond not only to preferred objects but also to non-preferred objects [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Differences in neural responses to ipsilateral stimuli in wide-view fields between face- and house-selective areas

Wang

Niu

et al. 2018

PLoS ONE

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Category-selective brain areas exhibit varying levels of neural activity to ipsilaterally presented stimuli. However, in face- and house-selective areas, the neural responses evoked by ipsilateral stimuli in the peripheral visual field remain unclear. In this study, we displayed face and house images using a wide-view visual presentation system while performing functional magnetic resonance imaging (fMRI). The face-selective areas (fusiform face area (FFA) and occipital face area (OFA)) exhibited intense neural responses to ipsilaterally presented images, whereas the house-selective areas (parahippocampal place area (PPA) and transverse occipital sulcus (TOS)) exhibited substantially smaller and even negative neural responses to the ipsilaterally presented images. We also found that the category preferences of the contralateral and ipsilateral neural responses were similar. Interestingly, the face- and house-selective areas exhibited neural responses to ipsilateral images that were smaller than the responses to the contralateral images. Multi-voxel pattern analysis (MVPA) was implemented to evaluate the difference between the contralateral and ipsilateral responses. The classification accuracies were much greater than those expected by chance. The classification accuracies in the FFA were smaller than those in the PPA and TOS. The closer eccentricities elicited greater classification accuracies in the PPA and TOS. We propose that these ipsilateral neural responses might be interpreted by interhemispheric communication through intrahemispheric connectivity of white matter connection and interhemispheric connectivity via the corpus callosum and occipital white matter connection. Furthermore, the PPA and TOS likely have weaker interhemispheric communication than the FFA and OFA, particularly in the peripheral visual field.

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“…Moreover, there are several emerging applications based in face recognition in augmented reality, gaming, security, and so on [3] [4] [13] [14]. Face recognition is also studied by neuroscientists and psychologists to provide useful insights in how the human brain works [15]. In such applications, features extracted from images or videos present high dimensionality and the sample availability for machine learning is scarce, potentially leading to the known curse of dimensionality [16].…”

Section: Introductionmentioning

confidence: 99%

Face recognition based on geodesic distance approximations between multivariate normal distributions

Soldera

Dodson

Scharcanski

2017

2017 IEEE International Conference on Imaging Systems and Techniques (IST)

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Geodesic distance is a natural dissimilarity measure between probability distributions of a specific type, and can be used to discriminate texture in image-based measurements. Furthermore, since there is no known closed-form solution for the geodesic distance between general multivariate normal distributions, we propose two efficient approximations to be used as texture dissimilarity metrics in the context of face recognition. A novel face recognition approach based on texture discrimination in high-resolution color face images is proposed, unlike the typical appearance-based approach that relies on low-resolution grayscale face images. In our face recognition approach, sparse facial features are extracted using predefined landmark topologies, that identify discriminative image locations on the face images. Given this landmark topology, the dissimilarity between distinct face images are scored in terms of the dissimilarities between their corresponding face landmarks, and the texture in each one of these landmarks is represented by multivariate normal distributions, expressing the color distribution in the vicinity of each landmark location. The classification of new face image samples occurs by determining the face image sample in the training set which minimizes the dissimilarity score, using the nearest neighbor rule. The proposed face recognition method was compared to methods representative of the state-of-the-art, using color or grayscale face images, and presented higher recognition rates. Moreover, the proposed texture dissimilarity metric also is efficient in general texture discrimination (e.g., texture recognition of material images), as our experiments suggest.

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The Neuroscience of Face Processing and Identification in Eyewitnesses and Offenders

Cited by 12 publications

References 162 publications

Anger provocation in violent offenders leads to emotion dysregulation

Anger provocation in violent offenders leads to emotion dysregulation

Differences in neural responses to ipsilateral stimuli in wide-view fields between face- and house-selective areas

Face recognition based on geodesic distance approximations between multivariate normal distributions

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