Distinct Facial Expressions Research Articles

AutYOLO-ATT: an attention-based YOLOv8 algorithm for early autism diagnosis through facial expression recognition

Autism Spectrum Disorder (ASD) is a developmental condition resulting from abnormalities in brain structure and function, which can manifest as communication and social interaction difficulties. Conventional methods for diagnosing ASD may not be effective in the early stages of the disorder. Hence, early diagnosis is crucial to improving the patient's overall health and well-being. One alternative and effective method for early autism diagnosis is facial expression recognition since autistic children typically exhibit distinct facial expressions that can aid in distinguishing them from other children. This paper provides a deep convolutional neural network (DCNN)-based real-time emotion recognition system for autistic kids. The proposed system is designed to identify six facial emotions, including surprise, delight, sadness, fear, joy, and natural, and to assist medical professionals and families in recognizing facial expressions in autistic children for early diagnosis and intervention. In this study, an attention-based YOLOv8 (AutYOLO-ATT) algorithm for facial expression recognition is proposed, which enhances the YOLOv8 model's performance by integrating an attention mechanism. The proposed method (AutYOLO-ATT) outperforms all other classifiers in all metrics, achieving a precision of 93.97%, recall of 97.5%, F1-score of 92.99%, and accuracy of 97.2%. These results highlight the potential of the proposed method for real-world applications, particularly in fields where high accuracy is essential.

Currency Classification Using Deep Learning

Among the uses of machine learning is the recognition of facial expressions. Based on the features that are derived from an image, it assigns a facial expression to one of the classes of facial expressions. Convolutional Neural Network (CNN) is a classification technique that may also be used to identify patterns in an image. We used the CNN approach to identify facial expressions in our proposed study. To increase the precision of facial emotion recognition, the wavelet transform is used after CNN processing. Seven distinct facial expressions are included in the facial expression image dataset that was obtained from Kaggle. The findings of the face expression recognition experiment utilizing CNN and wavelet transform show that the accuracy is improved, and the output is audible. Index Terms: Facial and Convolutional Neural Nets

Making Meaning of Inanimate Objects as Characters in The Blue Umbrella (2013)

This study aims to reveal deeper and different meaning of an inanimate object, specifically umbrella, from naked eye's perspective in The Blue Umbrella Film. The Blue Umbrella Film, an animated short film directed by Saschka Unsled, employs inanimate objects as the characters as seen through the distinct human's facial expression painted on the black, blue, and red umbrellas To pursue the aim of this study, qualitative method was conducted together with Saussure's semiotic theory. The results showed that the black umbrellas signified as the majorities, while the blue and red umbrellas signified as the minorities. The black umbrellas also signified as a group of office workers as well as members of Sexual and Gender Majority. On the other hand, the blue and the red umbrellas signified as the members of Sexual and Gender Minority. In short, the movie highlights an encouraging message that it is okay to be different from the majority's choice.

Novel Method for Three-Dimensional Facial Expression Recognition Using Self-Normalizing Neural Networks and Mobile Devices.

Introduction To date, most ways to perform facial expression recognition rely on two-dimensional images, advanced approaches with three-dimensional data exist. These however demand stationary apparatuses and thus lack portability and possibilities to scale deployment. As human emotions, intent and even diseases may condense in distinct facial expressions or changes therein, the need for a portable yet capable solution is signified. Due to the superior informative value of three-dimensional data on facial morphology and because certain syndromes find expression in specific facial dysmorphisms, a solution should allow portable acquisition of true three-dimensional facial scans in real time. In this study we present a novel solution for the three-dimensional acquisition of facial geometry data and the recognition of facial expressions from it. The new technology presented here only requires the use of a smartphone or tablet with an integrated TrueDepth camera and enables real-time acquisition of the geometry and its categorization into distinct facial expressions. Material and Methods Our approach consisted of two parts: First, training data was acquired by asking a collective of 226 medical students to adopt defined facial expressions while their current facial morphology was captured by our specially developed app running on iPads, placed in front of the students. In total, the list of the facial expressions to be shown by the participants consisted of “disappointed”, “stressed”, “happy”, “sad” and “surprised”. Second, the data were used to train a self-normalizing neural network. A set of all factors describing the current facial expression at a time is referred to as “snapshot”. Results In total, over half a million snapshots were recorded in the study. Ultimately, the network achieved an overall accuracy of 80.54% after 400 epochs of training. In test, an overall accuracy of 81.15% was determined. Recall values differed by the category of a snapshot and ranged from 74.79% for “stressed” to 87.61% for “happy”. Precision showed similar results, whereas “sad” achieved the lowest value at 77.48% and “surprised” the highest at 86.87%. Conclusions With the present work it can be demonstrated that respectable results can be achieved even when using data sets with some challenges. Through various measures, already incorporated into an optimized version of our app, it is to be expected that the training results can be significantly improved and made more precise in the future. Currently a follow-up study with the new version of our app that encompasses the suggested alterations and adaptions, is being conducted. We aim to build a large and open database of facial scans not only for facial expression recognition but to perform disease recognition and to monitor diseases’ treatment progresses.

Transfer Learning Technique with VGG-16 for Near-Infrared Facial Expression Recognition

In this paper, we investigate a deep learning vgg-16 network architecture for facial expression recognition under active near-infrared illumination condition and background. In particular, we consider the concept of transfer learning whereby features learned from high resolution images of huge datasets can be used to train a model of relatively small dataset without loosing the generalization ability. The pre-trained vgg-16 network architecture with transfer learning technique has been trained and validated on the Oulu-CASIA NIR dataset comprising of six (6) distinct facial expressions, and average test accuracy of 98.11% was achieved. The validation on our test data using the confusion, the precision, and the recall matrix reveals that our method achieves better results in comparison with the other methods in the literature.

How depressive symptoms and fear of negative evaluation affect feedback evaluation in social decision-making

• Individuals with symptoms of depression rejected more ultimatum game offers when the offer magnitude increased. • Positive emotional feedback was linked to a reward positivity, which did not occur in participants with higher symptoms of depression. • Both, individuals high in symptoms of depression and fear of negative evaluation approached sad feedback by rejecting more offers. The influence of emotional feedback on social decision-making situations is an omnipresent part of social interaction. Complementing existing research on the impact of approach- and avoidance-related personality traits, we examined the role of sub-clinical depression and fear of negative social evaluation in a modified ultimatum game. Different proposer identities reacted with distinct affective facial expressions to the acceptance and rejection of monetary offers in the ultimatum game. Facial expressions of proposers were relevant for depression and anxiety: A smile in response to acceptance or angry and sad feedback in response to rejection. People with higher symptoms of depression rejected more offers when the offer magnitude increased. Both, individuals with symptoms of depression and fear of negative evaluation approached sad feedback by rejecting more offers. At the neural level, positive emotional feedback (i.e., a smiling face) was linked to a reward positivity, which did not occur in participants with higher symptoms of depression. Since our paradigm implies a decision-dependent feedback pattern, the negative feedback might have been expected and was therefore not perceived as particularly relevant. The results indicate specific behavioral as well as neural reaction patterns, which can be used especially for an initial subclinical identification of depression.

Gamifying the teaching of facial expressions for children with autistic spectrum disorder

The game is an educational tool developed by the multidisciplinary team of the Federal Institute of Education Science and Technology of Maranhão, composed of professors, students and volunteers, to be applied in the daily life of children with disorder of Autistic Spectre. It follows the differentiated teaching model, because it aims to assist in the treatment of autistic children through the recognition and interpretations of facial expressions, in their various spectra (mild, moderate or severe), exercising their stimuli and Cognitive ability to recognize distinct facial expressions through mobile devices in a multiple choice environment, allowing the gradual increase of the autistic's sensitivity to external stimuli, a predilection for facial images that are They handle repetitively, developing the wearer's motricity, improving their interpersonal relationship. The methodology used is Applied Behavior Analysis (ABA), commonly associated with the treatment of people with autism spectrum disorders using positive reinforcements, thus contributing to the teaching and practice effectively based on Evidence, because it consists of basic, applied and theoretical research, through social behaviors and patterns. The research was distinguished, in the academic and scientific environment, with three works published in international events.

A Novel Framework design for Semantic based Image retrieval as a Cyber Forensic Tool

Cyber forensics includes the areas of computer forensics, network forensics and internet forensics. Digital images were commonly used in cyber forensics to collect criminal images, fingerprints, images of crime events and so on. Since the current cyber forensic tools are not very much furnished with the course of action of huge image data, it becomes a big issue to obtain image evidence to prosecute the criminal, most of the evidence is available in the form of raw semantics. Cyber forensic investigators often face the challenge of manually examining an enormous quantity of digital image information to identify direct evidence with the assistance of this semantics. Semantic based image retrieval system (SBIR) is therefore the recent and best option to solve this drawback. The main purpose of this research is to design for cyber forensic tools a novel framework of semantic-based image retrieval system. We therefore present a deep learning framework based on the Convolution neural network(GoogLeNet) for recognizing distinct facial expressions from the Yale facial image database for cyber associated forensic tools, the presented framework is very effective for classification and detection based on semantics or verbal descriptions. The network has accomplished a decent accuracy of 86.25 % after training.

The influence of context on distinct facial expressions of disgust.

Face perception is susceptible to contextual influence and perceived physical similarities between emotion cues. However, studies often use structurally homogeneous facial expressions, making it difficult to explore how within-emotion variability in facial configuration affects emotion perception. This study examined the influence of context on the emotional perception of categorically identical, yet physically distinct, facial expressions of disgust. Participants categorized two perceptually distinct disgust facial expressions, "closed" (i.e., scrunched nose, closed mouth) and "open" (i.e., scrunched nose, open mouth, protruding tongue), that were embedded in contexts comprising emotion postures and scenes. Results demonstrated that the effect of nonfacial elements was significantly stronger for "open" disgust facial expressions than "closed" disgust facial expressions. These findings provide support that physical similarity within discrete categories of facial expressions is mutable and plays an important role in affective face perception. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Compassionate faces: Evidence for distinctive facial expressions associated with specific prosocial motivations

Compassion is a complex cognitive, emotional and behavioural process that has important real-world consequences for the self and others. Considering this, it is important to understand how compassion is communicated. The current research investigated the expression and perception of compassion via the face. We generated exemplar images of two compassionate facial expressions induced from two mental imagery tasks with different compassionate motivations (Study 1). Our kind- and empathic compassion faces were perceived differently and the empathic-compassion expression was perceived as best depicting the general definition of compassion (Study 2). Our two composite faces differed in their perceived happiness, kindness, sadness, fear and concern, which speak to their underling motivation and emotional resonance. Finally, both faces were accurately discriminated when presented along a compassion continuum (Study 3). Our results demonstrate two perceptually and functionally distinct facial expressions of compassion, with potentially different consequences for the suffering of others.

Distinct facial expressions represent pain and pleasure across cultures

Real-world studies show that the facial expressions produced during pain and orgasm-two different and intense affective experiences-are virtually indistinguishable. However, this finding is counterintuitive, because facial expressions are widely considered to be a powerful tool for social interaction. Consequently, debate continues as to whether the facial expressions of these extreme positive and negative affective states serve a communicative function. Here, we address this debate from a novel angle by modeling the mental representations of dynamic facial expressions of pain and orgasm in 40 observers in each of two cultures (Western, East Asian) using a data-driven method. Using a complementary approach of machine learning, an information-theoretic analysis, and a human perceptual discrimination task, we show that mental representations of pain and orgasm are physically and perceptually distinct in each culture. Cross-cultural comparisons also revealed that pain is represented by similar face movements across cultures, whereas orgasm showed distinct cultural accents. Together, our data show that mental representations of the facial expressions of pain and orgasm are distinct, which questions their nondiagnosticity and instead suggests they could be used for communicative purposes. Our results also highlight the potential role of cultural and perceptual factors in shaping the mental representation of these facial expressions. We discuss new research directions to further explore their relationship to the production of facial expressions.

Development of three-dimensional facial expression models using morphing methods for fabricating facial prostheses

PurposeIt is essential to fabricate a best-fit three-dimensional (3D) facial prosthesis model capable of facial expressions. In order for the facial prosthesis to remain in position, especially around marginal areas subject to movement, a new method of making 3D facial expression models using time-series data allowing changes in facial expression by morphing technique was developed. MethodsSeven normal subjects and seven patients with nasal defects or nasal deformities participated in this study. Three distinct facial expressions (i.e., a neutral expression, smiled, and open mouthed) were digitally acquired with a facial scanner. Prepared template models were transformed to homologous models, which can represent the form as shape data with the same number of point cloud data of the same topology referring to the scanning data. Finally, 3D facial expression models were completed by generating a morphing image based on two sets of homologous models, and the accuracy of the homologous models of all subjects was evaluated. Results3D facial expression models of both normal subjects and patients with nasal defects were successfully generated. No significant differences in shape between the scanned models and homologous models were shown. ConclusionsThe high accuracy of this 3D facial expression model in both normal subjects and patients suggests its use for fabricating facial prostheses.

Changes in facial expressions following functional orthopaedic treatment for Class II division 1 malocclusion: a prospective controlled study.

The aim of this study was to quantify changes in facial expressions in children with Class II division 1 malocclusion treated with functional appliances. A prospective controlled study was carried out evaluating 20 children with Class II division 1 malocclusion (ages 9-13) treated with a functional appliance for 12 months (treatment group). Age- and gender-matched children without immediate orthodontic treatment need were followed up without treatment for 12 months (control group). A frame-mode video sequence was taken of each subject at the beginning and end of the 12-month study period, during which 5 distinct facial expressions (posed smile, spontaneous smile, aggressive smile, lip pucker, and maximal mouth opening) were recorded. Frames at which each facial expression was at its peak were analysed. Changes in horizontal and vertical facial anthropometric distances brought about by performing each facial expression were measured. Intragroup differences following treatment were assessed using paired t-tests, whereas intergroup differences were assessed using unpaired t-tests. All treated children showed an improvement in dentoalveolar relationships. Initial facial expressions differed between groups, with the treatment group showing less marked changes during lip pucker and more marked changes during maximal mouth opening. In contrast, no statistically significant differences were found between groups for the final facial expressions. This study suggests that functional appliance use in growing children with Class II division 1 malocclusion tends to normalize soft-tissue movements during facial expressions.

Supplemental Material for The Influence of Context on Distinct Facial Expressions of Disgust

Supplemental Material for The Influence of Context on Distinct Facial Expressions of Disgust

When your face describes your memories: facial expressions during retrieval of autobiographical memories.

Thanks to the current advances in the software analysis of facial expressions, there is a burgeoning interest in understanding emotional facial expressions observed during the retrieval of autobiographical memories. This review describes the research on facial expressions during autobiographical retrieval showing distinct emotional facial expressions according to the characteristics of retrieved memoires. More specifically, this research demonstrates that the retrieval of emotional memories can trigger corresponding emotional facial expressions (e.g. positive memories may trigger positive facial expressions). Also, this study demonstrates the variations of facial expressions according to specificity, self-relevance, or past versus future direction of memory construction. Besides linking research on facial expressions during autobiographical retrieval to cognitive and affective characteristics of autobiographical memory in general, this review positions this research within the broader context research on the physiologic characteristics of autobiographical retrieval. We also provide several perspectives for clinical studies to investigate facial expressions in populations with deficits in autobiographical memory (e.g. whether autobiographical overgenerality in neurologic and psychiatric populations may trigger few emotional facial expressions). In sum, this review paper demonstrates how the evaluation of facial expressions during autobiographical retrieval may help understand the functioning and dysfunctioning of autobiographical memory.

Temporal integration of bodies and faces: united we stand, divided we fall?

ABSTRACTAlthough most research in the field of emotion perception has focused on the isolated face, recent studies have highlighted the integration of emotional faces and bodies. Regardless of instructions to ignore it, incongruent emotional body context can automatically alter the categorization of distinct and prototypical facial expressions. Previous work suggested that face–body integration is rapid, automatic, and persists even after spatial misalignment of the two. However, the temporal dynamics of face–body integration were never explored. Using a novel measure of temporal visual integration, the current report examines the effect of introducing a temporal gap between the body and face. When presented simultaneously, faces and bodies showed robust integration: the face was strongly influenced by the information conveyed by the task-irrelevant body. By contrast, when faces and bodies were presented with even the briefest temporal lag, we failed to find evidence for integration of bodily and facial emotion cues. These main findings were replicated across three experiments, and suggest that the integration between emotional faces and bodies may be more fragile than previously assumed.

Face Recognition using SIFT Key with Optimal Features Selection Model

Facial expression is complex in nature due to legion of variations present. These variations are identified and recorded using feature extraction mechanisms. The researchers have worked towards it and created classifiers for identifying face expression. The classifiers involve Principal component analysis (PCA), Local Polynomial approximation (LPA), Linear binary pattern (LBP), Discrete wavelet transformation (DWT) etc. The proposed work deals with the new classifier using SIFT key with genetic algorithm to identify distinct facial expression. Optimal features of existing algorithms are used within the proposed work. Also comparison of existing techniques such as LBP, PCA and DWT is presented with SIFT key with genetic algorithm. The results show that proposed classifier gives better result in terms of recognition raet.

Primal emotional-affective expressive foundations of human facial expression

Emotional facial expressions provide important insights into various valenced feelings of humans. Recent cross-species neuroscientific advances offer insights into molecular foundations of mammalian affects and hence, by inference, the related emotional/affective facial expressions in humans. This is premised on deep homologies based on affective neuroscience studies of valenced primary emotional systems across species. Thus, emerging theoretical perspectives suggest that ancient cross-species emotional systems are intimately linked not only to emotional action patterns evident in all mammals, but also, by inference, distinct emotional facial expressions studied intensively in humans. Thus, the goal of the present theoretical work was to relate categories of human emotional facial expressions—e.g. especially of anger fear, joy and sadness—to respective underlying primary cross-mammalian emotional circuits. This can potentially provide coherent theoretical frameworks for the eventual molecular study of emotional facial expressions in humans.

Visual exploration and discrimination of emotional facial expressions in 3-, 7- and 12-month-old infants

The first year of life is critical in the development of the abilities to process facial expressions. Numerous studies have investigated discrimination and categorization of distinct facial expressions of emotion. However, infants' visual exploratory strategies of these facial expressions and their developmental paths remain unclear. The perfection of eye movement tracking systems makes now the detailed analysis of facial exploration of faces feasible, and hence facilitates the identification of the features in facial expressions which infants focus on. In this study, oculometric parameters of 3- (n=36), 7- (n=66) and 12-month-old infants (n=59) were collected while facial expressions were displayed to them. Infants were consecutively exposed to 6 x 2 (randomized direct/averted gaze) 6-second trials consisting in the presentation of a full-front female or male avatar face expressing anger, disgust, fear, joy, sadness or neutrality. RESULTS indicate a significant main effect of age on visual exploration: while 3-month-olds spent most of their time exploring eyes and external traits, 7- and 12-month-olds explored significantly less the external traits and focused more on mouth and nose. Further, the avatars' facial expressions had no effect on 3-month-olds' visual exploration; as compared to the 7- and 12-month-olds who differentially explored distinctive facial features depending on the facially-displayed emotion. In conclusion, our data suggest that, along the first year, infants' visual exploration gradually develops to focus on relevant internal features, with differentiated attention to critical facial features depending on the emotion displayed. Meeting abstract presented at VSS 2015. Language: en

Doing It With Feeling: The Emotion in Early Socioemotional Development

Carroll Izard’s theoretical and research contributions to the study of early socioemotional development are profiled. His studies of early emotional expression and the formulations of differential emotions theory have stimulated contemporary inquiry into the organization of early emotional life, the developmental processes by which distinct feelings and facial expressions become progressively concordant, and how the emotional expressions of others become imbued with emotion meaning. His work on emotion, attachment, and emotion–cognition relations has contributed to contemporary study of the emotional bases of attachment organization and the development of the internal working models associated with attachment security. Because of Izard’s contributions, developmental emotions research is theoretically richer, and emotion has a more central place in our understanding of development and motivation.