Recognition Of Basic Facial Expressions Research Articles

Visual Field Restriction in the Recognition of Basic Facial Expressions: A Combined Eye Tracking and Gaze Contingency Study.

Uncertainties and discrepant results in identifying crucial areas for emotional facial expression recognition may stem from the eye tracking data analysis methods used. Many studies employ parameters of analysis that predominantly prioritize the examination of the foveal vision angle, ignoring the potential influences of simultaneous parafoveal and peripheral information. To explore the possible underlying causes of these discrepancies, we investigated the role of the visual field aperture in emotional facial expression recognition with 163 volunteers randomly assigned to three groups: no visual restriction (NVR), parafoveal and foveal vision (PFFV), and foveal vision (FV). Employing eye tracking and gaze contingency, we collected visual inspection and judgment data over 30 frontal face images, equally distributed among five emotions. Raw eye tracking data underwent Eye Movements Metrics and Visualizations (EyeMMV) processing. Accordingly, the visual inspection time, number of fixations, and fixation duration increased with the visual field restriction. Nevertheless, the accuracy showed significant differences among the NVR/FV and PFFV/FV groups, despite there being no difference in NVR/PFFV. The findings underscore the impact of specific visual field areas on facial expression recognition, highlighting the importance of parafoveal vision. The results suggest that eye tracking data analysis methods should incorporate projection angles extending to at least the parafoveal level.

Synchronized or not: Developmental characteristics of basic facial expression recognition in infants

Synchronized or not: Developmental characteristics of basic facial expression recognition in infants

Facial Motion Analysis beyond Emotional Expressions.

Facial motion analysis is a research field with many practical applications, and has been strongly developed in the last years. However, most effort has been focused on the recognition of basic facial expressions of emotion and neglects the analysis of facial motions related to non-verbal communication signals. This paper focuses on the classification of facial expressions that are of the utmost importance in sign languages (Grammatical Facial Expressions) but also present in expressive spoken language. We have collected a dataset of Spanish Sign Language sentences and extracted the intervals for three types of Grammatical Facial Expressions: negation, closed queries and open queries. A study of several deep learning models using different input features on the collected dataset (LSE_GFE) and an external dataset (BUHMAP) shows that GFEs can be learned reliably with Graph Convolutional Networks simply fed with face landmarks.

Pain intensity recognition via multi‐scale deep network

Similar to the basic facial expression recognition, one challenge for pain intensity recognition is some individual characteristics, e.g. face shapes, may cause great diversities in the same emotion. So it is usually very difficult to distinguish two adjacent intensity levels of pain expression as each intensity has a large variation. In this study, a coarse-to-fine combination method is proposed for pain intensity recognition. The results of multi-scale outputs from multiple base deep network are combined in a probabilistic way for improving the discrimination between visually similar adjacent levels. A two-layer tree classifier is proposed in a multi-task framework for pain intensity recognition as well as face shape recognition, replacing the planar Softmax classifier in each base deep network. In the first layer of tree classifier, multi-scale classifiers are constructed for recognizing facial pain intensities and the conventional classifiers are constructed for face shape recognition in the second layer. Finally, the tree classifier including multi-scale classifiers and conventional classifiers is jointly optimised during the training phase and only high level classifiers are used for recognising pain intensities in the test phase. The extensive experiments on UNBC shoulder pain dataset show the proposed method gets promising results in pain intensity recognition.

The asynchronous influence of facial expressions on bodily expressions

The ability to extract correct emotional information from facial and bodily expressions is fundamental for the development of social skills. Previous studies have shown that bodily expressions affect the recognition of basic facial expressions dramatically. However, few studies have considered the view that facial expressions may influence the recognition of bodily expressions. Further, previous studies have failed to consider a comprehensive set of emotional categories. The present study sought to examine whether facial expressions would impact the recognition of bodily expressions asynchronously, using four basic emotions. Participants performed an affective priming task, in which the priming stimuli included four facial expressions (happy, sad, fearful, and angry), and the target stimuli were bodily expressions matching the same emotions. The results indicated that the perception of affective facial expressions significantly influenced the accuracy and reaction time for body-based emotion categorization, particularly for bodily expression of happiness. The recognition accuracy of congruent expressions was higher, relative to that of incongruent expressions. The findings show that facial expressions influence the recognition of bodily expressions, despite the asynchrony.

Recognition of dynamic and static facial expressions of emotion among older adults with major depression.

The recognition of facial expressions of emotion is essential to living in society. However, individuals with major depression tend to interpret information considered imprecise in a negative light, which can exert a direct effect on their capacity to decode social stimuli. To compare basic facial expression recognition skills during tasks with static and dynamic stimuli in older adults with and without major depression. Older adults were selected through a screening process for psychiatric disorders at a primary care service. Psychiatric evaluations were performed using criteria from the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5). Twenty-three adults with a diagnosis of depression and 23 older adults without a psychiatric diagnosis were asked to perform two facial emotion recognition tasks using static and dynamic stimuli. Individuals with major depression demonstrated greater accuracy in recognizing sadness (p=0.023) and anger (p=0.024) during the task with static stimuli and less accuracy in recognizing happiness during the task with dynamic stimuli (p=0.020). The impairment was mainly related to the recognition of emotions of lower intensity. The performance of older adults with depression in facial expression recognition tasks with static and dynamic stimuli differs from that of older adults without depression, with greater accuracy regarding negative emotions (sadness and anger) and lower accuracy regarding the recognition of happiness.

Influence of gender in the recognition of basic facial expressions: A critical literature review.

To conduct a systematic literature review about the influence of gender on the recognition of facial expressions of six basic emotions. We made a systematic search with the search terms (face OR facial) AND (processing OR recognition OR perception) AND (emotional OR emotion) AND (gender or sex) in PubMed, PsycINFO, LILACS, and SciELO electronic databases for articles assessing outcomes related to response accuracy and latency and emotional intensity. The articles selection was performed according to parameters set by COCHRANE. The reference lists of the articles found through the database search were checked for additional references of interest. In respect to accuracy, women tend to perform better than men when all emotions are considered as a set. Regarding specific emotions, there seems to be no gender-related differences in the recognition of happiness, whereas results are quite heterogeneous in respect to the remaining emotions, especially sadness, anger, and disgust. Fewer articles dealt with the parameters of response latency and emotional intensity, which hinders the generalization of their findings, especially in the face of their methodological differences. The analysis of the studies conducted to date do not allow for definite conclusions concerning the role of the observer's gender in the recognition of facial emotion, mostly because of the absence of standardized methods of investigation.

Recognition of facial expressions by alcoholic patients: a systematic literature review.

BackgroundAlcohol abuse and dependence can cause a wide variety of cognitive, psychomotor, and visual-spatial deficits. It is questionable whether this condition is associated with impairments in the recognition of affective and/or emotional information. Such impairments may promote deficits in social cognition and, consequently, in the adaptation and interaction of alcohol abusers with their social environment. The aim of this systematic review was to systematize the literature on alcoholics’ recognition of basic facial expressions in terms of the following outcome variables: accuracy, emotional intensity, and latency time.MethodsA systematic literature search in the PsycINFO, PubMed, and SciELO electronic databases, with no restrictions regarding publication year, was employed as the study methodology.ResultsThe findings of some studies indicate that alcoholics have greater impairment in facial expression recognition tasks, while others could not differentiate the clinical group from controls. However, there was a trend toward greater deficits in alcoholics. Alcoholics displayed less accuracy in recognition of sadness and disgust and required greater emotional intensity to judge facial expressions corresponding to fear and anger.ConclusionThe current study was only able to identify trends in the chosen outcome variables. Future studies that aim to provide more precise evidence for the potential influence of alcohol on social cognition are needed.

Facial Expression Recognition Based on Block Gabor Wavelet Fusion Feature

Considering the complexity of facial expressions, this paper adopts the strategy of face segmentation for facial expression auto-recognition. On the training dataset, feature-based Gabor wavelets are extracted for each facial block. Then classification is done to realize the basic facial expression recognition to determine which part contributes most for a given expression. A binary weight matrix is assigned a weight of ‘1’ for highest such recognition rate, otherwise ’0’. At the test stage, the voted feature is calculated for each basic expression by multiplying the weight matrix and than the relative recognition rates are obtained. Finally the target expression is determined by voting. The experiment results show that Gabor feature plays better roles in representing the express than PCA and DCT. Based on the learning the optimal area wavelet modeling expressions, relative to the entire facial feature extraction, the system has better recognition effect.

Early visual experience and the recognition of basic facial expressions: involvement of the middle temporal and inferior frontal gyri during haptic identification by the early blind.

Face perception is critical for social communication. Given its fundamental importance in the course of evolution, the innate neural mechanisms can anticipate the computations necessary for representing faces. However, the effect of visual deprivation on the formation of neural mechanisms that underlie face perception is largely unknown. We previously showed that sighted individuals can recognize basic facial expressions by haptics surprisingly well. Moreover, the inferior frontal gyrus (IFG) and posterior superior temporal sulcus (pSTS) in the sighted subjects are involved in haptic and visual recognition of facial expressions. Here, we conducted both psychophysical and functional magnetic-resonance imaging (fMRI) experiments to determine the nature of the neural representation that subserves the recognition of basic facial expressions in early blind individuals. In a psychophysical experiment, both early blind and sighted subjects haptically identified basic facial expressions at levels well above chance. In the subsequent fMRI experiment, both groups haptically identified facial expressions and shoe types (control). The sighted subjects then completed the same task visually. Within brain regions activated by the visual and haptic identification of facial expressions (relative to that of shoes) in the sighted group, corresponding haptic identification in the early blind activated regions in the inferior frontal and middle temporal gyri. These results suggest that the neural system that underlies the recognition of basic facial expressions develops supramodally even in the absence of early visual experience.

Texture-independent recognition of facial expressions in image snapshots and videos

This paper addresses the static and dynamic recognition of basic facial expressions. It has two main contributions. First, we introduce a view- and texture-independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker. We represent the learned facial actions associated with different facial expressions by time series. Second, we compare this dynamic scheme with a static one based on analyzing individual snapshots and show that the former performs better than the latter. We provide evaluations of performance using three subspace learning techniques: linear discriminant analysis, non-parametric discriminant analysis and support vector machines.

The automaticity of emotional face-context integration.

Recent studies have demonstrated that context can dramatically influence the recognition of basic facial expressions, yet the nature of this phenomenon is largely unknown. In the present paper we begin to characterize the underlying process of face-context integration. Specifically, we examine whether it is a relatively controlled or automatic process. In Experiment 1 participants were motivated and instructed to avoid using the context while categorizing contextualized facial expression, or they were led to believe that the context was irrelevant. Nevertheless, they were unable to disregard the context, which exerted a strong effect on their emotion recognition. In Experiment 2, participants categorized contextualized facial expressions while engaged in a concurrent working memory task. Despite the load, the context exerted a strong influence on their recognition of facial expressions. These results suggest that facial expressions and their body contexts are integrated in an unintentional, uncontrollable, and relatively effortless manner.

Improving dynamic facial expression recognition with feature subset selection

This paper addresses the dynamic recognition of basic facial expressions in videos using feature subset selection. Feature selection has been already used by some static classifiers where the facial expression is recognized from one single image. Past work on dynamic facial expression recognition has emphasized the issues of feature extraction and classification, however, less attention has been given to the critical issue of feature selection in the dynamic scenario. The main contributions of the paper are as follows. First, we show that dynamic facial expression recognition can be casted into a classical classification problem. Second, we combine a facial dynamics extractor algorithm with a feature selection scheme for generic classifiers. We show that the paradigm of feature subset selection with a wrapper technique can improve the dynamic recognition of facial expressions. We provide evaluations of performance on real video sequences using five standard machine learning approaches: Support Vector Machines, K Nearest Neighbor, Naive Bayes, Bayesian Networks, and Classification Trees.

An investigation of basic facial expression recognition in autism spectrum disorders

This study was designed to test three competing hypotheses (impaired configural processing; impaired Theory of Mind; atypical amygdala functioning) to explain the basic facial expression recognition profile of adults with autism spectrum disorders (ASD). In Experiment 1 the Ekman and Friesen (1976) series were presented upright and inverted. Individuals with ASD were significantly less accurate than controls at recognising upright facial expressions of fear, sadness and disgust and their pattern of errors suggested some configural processing difficulties. Impaired recognition of inverted facial expressions suggested some additional difficulties processing the facial features. Unexpectedly, the clinical group misidentified fear as anger. In Experiment 2 feature processing of facial expressions was investigated by presenting stimuli in a piecemeal fashion, starting with either just the eyes or the mouth. Individuals with ASD were impaired at recognising fear from the eyes and disgust from the mouth; they also confused fearful eyes as being angry. The findings are discussed in terms of the three competing hypotheses tested.