Facial emotion recognition using deep quantum and advanced transfer learning mechanism.

One of the ways humans communicate is by using facial expressions. Research on technology development in artificial intelligence uses deep learning methods in human and computer interactions as an effective system application process. One example, if someone does show and tries to recognize facial expressions when communicating. The prediction of the expression or emotion of some people who see it sometimes does not understand. In psychology, the detection of emotions or facial expressions requires analysis and assessment of decisions in predicting a person's emotions or group of people in communicating. This research proposes the design of a system that can predict and recognize the classification of facial emotions based on feature extraction using the Convolution Neural Network (CNN) algorithm in real-time with the OpenCV library, namely: TensorFlow and Keras. The research design implemented in the Raspberry Pi consists of three main processes, namely: face detection, facial feature extraction, and facial emotion classification. The prediction results of facial expressions in research with the Convolutional Neural Network (CNN) method using Facial Emotion Recognition (FER-2013) were 65.97% (sixty-five point ninety-seven percent).

Different styles of social interaction are one of the core characteristics of autism spectrum disorder (ASD). Social differences among individuals with ASD often include difficulty in discerning the emotions of neurotypical people based on their facial expressions. This review first covers the rich body of literature studying differences in facial emotion recognition (FER) in those with ASD, including behavioral studies and neurological findings. In particular, we highlight subtle emotion recognition and various factors related to inconsistent findings in behavioral studies of FER in ASD. Then, we discuss the dual problem of FER – namely facial emotion expression (FEE) or the production of facial expressions of emotion. Despite being less studied, social interaction involves both the ability to recognize emotions and to produce appropriate facial expressions. How others perceive facial expressions of emotion in those with ASD has remained an under-researched area. Finally, we propose a method for teaching FER [FER teaching hierarchy (FERTH)] based on recent research investigating FER in ASD, considering the use of posed vs. genuine emotions and static vs. dynamic stimuli. We also propose two possible teaching approaches: (1) a standard method of teaching progressively from simple drawings and cartoon characters to more complex audio-visual video clips of genuine human expressions of emotion with context clues or (2) teaching in a field of images that includes posed and genuine emotions to improve generalizability before progressing to more complex audio-visual stimuli. Lastly, we advocate for autism interventionists to use FER stimuli developed primarily for research purposes to facilitate the incorporation of well-controlled stimuli to teach FER and bridge the gap between intervention and research in this area.

BackgroundThe emotional state of individuals is difficult to identify and it is developing now a days because of vast interest in recognition. Many technologies have been developed to identify this emotional expression based on facial expressions, vocal expressions, physiological signals, and body expressions. Among these, facial emotion is very expressive for recognition using multimodalities. Understanding facial emotions has applications in mental well‐being, decision‐making, and even social change, as emotions play a crucial role in our lives. This recognition is complicated by the high dimensionality of data and non‐linear interactions across modalities. Moreover, the way emotion is expressed by people varies and these feature identification remains challenging, where these limitations are overcome by Deep learning models.MethodsThis research work aims at facial emotion recognition through the utilization of a deep learning model, named the proposed Residual Fused‐Graph Convolution Network (RF‐GCN). Here, multimodal data included is video as well as an Electroencephalogram (EEG) signal. Also, the Non‐Local Means (NLM) filter is used for pre‐processing input video frames. Here, the feature selection process is carried out using chi‐square, after feature extraction, which is done in both pre‐processed video frames and input EEG signals. Finally, facial emotion recognition and its types are determined by RF‐GCN, which is a combination of both the Deep Residual Network (DRN) and Graph Convolutional Network (GCN).ResultsFurther, RF‐GCN is evaluated for performance by metrics such as accuracy, recall, and precision, with superior values of 91.6%, 96.5%, and 94.7%.ConclusionsRF‐GCN captures the nuanced relationships between different emotional states and improves recognition accuracy. The model is trained and evaluated on the dataset and reflects real‐world conditions.

The ability to recognize emotion is important to wellbeing and building relationships with others, making this skill important in adolescence. Research investigating adolescents’ ability to recognize facial and vocal emotion expressions has reported differing conclusions about the pattern of emotion recognition across this developmental period. This systematic review aimed to clarify the pattern of recognition for facial and vocal emotion expressions, and the relationship of performance to different task and emotion expression characteristics. A comprehensive and systematic search of the literature was conducted using six databases. To be eligible for inclusion, studies had to report data for adolescents between 11 and 18 years of age and measure accuracy of the recognition of emotion cues in either the face or voice. A total of 2333 studies were identified and 47 met inclusion criteria. The majority of studies focused on facial emotion recognition. Overall, early, mid-, and late-adolescents showed a similar pattern of recognition for both facial and vocal emotion expressions with the exception of Sad facial expressions. Sex of the participant also had minimal impact on the overall recognition of different emotions. However, analysis showed considerable variability according to task and emotion expression characteristics. Future research needs to increase focus on recognition of complex emotions, and low-intensity emotion expressions as well as the influence of the inclusion of Neutral as a response option.

Automated facial emotion recognition (FER) is one of the important fields of human-computer interaction (HCI). FER remains challenging due to facial accessories, non-uniform illumination, pose variation, etc. Emotion detection exploiting conventional algorithms has the demerit of mutual optimization of classification and feature extraction. Artificial intelligence (AI) techniques can be employed to identify FER automatically. Deep learning (DL) driven FER models have recently allowed for designing an end-to-end learning process. Therefore, this study designs a Henry Gas Solubility Optimization with Deep Learning Based FER (HGSO-DLFER) technique for HCI. The HGSO-DLFER technique aims to recognize and identify various kinds of facial emotions. To accomplish this, the HGSO-DLFER technique employs adaptive fuzzy filtering (AFF) for noise removal. In addition, the MobileNet model is used for feature vector generation, and the HGSO algorithm optimally chooses its hyperparameter scan. For the recognition of facial emotions, the HGSO-DLFER technique uses an autoencoder (AE) classifier with a Nadam optimizer. A widespread experimental analysis is made to facilitate a better understanding of the FER results by the HGSO-DLFER technique. The comparative analysis showed the effective performance of the HGSO-DLFER technique over other FER techniques with maximum accuracy of 98.65%.

The authors examine facial emotion recognition and unirhinal olfactory performance in 19 schizophrenia patients and 14 comparison subjects. In patients, right nostril odor identification performance was positively related to overall emotion recognition accuracy, specifically, sad facial expressions. Olfactory and emotion recognition abilities appear significantly linked in schizophrenia.

Humans often express themselves through facial expressions. Deep learning techniques are used as an efficient system application process in research on the advancement of artificial intelligence technology in human-computer interactions. As an illustration, let’s say someone tries to communicate by using facial expressions. Some people who see it occasionally cannot foresee the expression or emotion it may evoke. Psychology includes study and evaluation of inferences in interpreting a person’s or group of people’s emotions when interacting in order to recognize emotions or facial expressions. Indeed, a convolutional neural networks (CNN) model may be learned to assess images and recognize facial expressions. This study suggests developing a system that can classify and forecast facial emotions using feature extraction and real-time Convolution Neural Network (CNN) technology from the OpenCV library. We have chosen FER 2013 Dataset as the main dataset for our study. Face detection, extraction of facial features, and facial emotion categorization are the three key procedures that make up the research that was implemented.

Sixty healthy women were included to the study. They were divided two group labeled women with PMS (n=33) and without PMS (n=27), which is accordance with the Premenstrual Assessment Form. Then, The Facial Emotion Recognition Test (56 mixed photos with happy, surprised, fearful, sad, angry, disgusted and neutral facial expressions from Ekman & Friesen's series) was performed on each group in both the luteal and follicular phases. The women with PMS were significantly worse in recognizing sad (p=0.003) and surprised (p=0.019) faces in the luteal phase compared to the follicular phase, whereas women without PMS were significantly worse in recognizing sad faces (p=0.008) in the luteal phase compared to the follicular phase. There were no significant differences between women with and without PMS in either the luteal phases or in the follicular phases according to facial emotion recognition (for each, p>0.05). Conclusıon: The women with PMS do not differ from women without PMS in recognizing facial emotions accurately. The low accuracy rate in the recognition of sad and surprised facial emotions in the luteal phase may lead PMS women to have more social problems.

To examine the impact of music as a cross-modal prime on facial emotion recognition ability in autistic children, this study compares the priming effect of music with that of faces as an intramodal prime and nonverbal sounds as the same cross-modal prime. The response time and accuracy of facial emotion recognition (happy and sad) were compared among 21 neurotypical children and 17 autistic children under various priming stimuli. A data analysis revealed that autistic children exhibited worse recognition of facial emotional expressions and demonstrated longer reaction time than neurotypical children. Unlike the other two stimuli (facial expressions and nonverbal sounds), music as a cross-modal prime resulted in slightly higher response accuracy for emotionally congruent conditions compared with emotionally incongruent conditions among autistic children. Furthermore, reaction time was significantly prolonged under emotionally congruent conditions than under emotionally incongruent conditions. This suggests that autistic children demonstrate a greater propensity to allocate additional time to improve the accuracy of their judgments under congruent conditions compared with incongruent conditions. Therefore, music intervention has significant potential for supporting autistic children to empathize with faces by playing emotionally congruent music and improving their facial emotion recognition abilities.

Many children with autism spectrum disorders (ASDs) demonstrate facial emotion recognition and expression impairments. These impairments may contribute to social disability and may put children with ASDs at risk for developing further mental health problems. In this pilot study, we examined the use of a coach- and computer-assisted facial emotion training program for children with ASDs. The intervention components focused on (a) increasing attention to relevant facial emotion cues, (b) increasing facial emotion recognition speed, and (c) using imitation to build skills of facial emotion expression. Three pilot participants demonstrated improved facial emotion recognition (accuracy and speed) of dynamic and static presentations of facial expressions and self-expression. Some improvements persisted 5 weeks after training. Results support the acceptability and feasibility of the training program. These preliminary findings are promising and suggest the need for replication with larger samples and further assessment of acceptability, feasibility, and efficacy.

People living with Human Immunodeficiency Virus (PLHIV) have been reported to show poor facial emotion recognition. However, these studies presented participants with facial emotion photographs whereas in real life facial emotion recognition hardly involves inferring emotions from static faces. Moreover, emotion recognition from other sensory modalities, such as auditory, has hardly been explored. There’s also a dearth of studies examining emotion regulation difficulties in this group. The present study, thus, explored facial (using facial emotion videos) and auditory emotion recognition as well as difficulties in emotion regulation (using the Hindi version of Difficulties in Emotion Regulation Scale) in 60 PLHIV and 60 people without HIV (PWoHIV). Additionally, the association of HIV duration (duration since diagnosis of HIV), viral load, and Clusters of differentiation 4 (CD4) count with emotion recognition and regulation difficulties in PLHIV was explored. Findings from one-way ANCOVA (with education and socioeconomic status as covariates) revealed significantly impaired auditory emotion recognition (particularly for fear) among PLHIV than PWoHIV. The former also showed significantly poorer facial emotion recognition for surprise. PLHIV also self-reported significantly more emotion regulation difficulties than PWoHIV, specifically Nonacceptance of their response to negative emotions and limited access to emotion regulation Strategies. CD4 count was negatively correlated with emotion regulation difficulties, particularly for accomplishing goal-directed behaviour when experiencing negative emotions (Goals) and Strategies. Besides the novel addition to the literature regarding impaired auditory emotion recognition in PLHIV, these findings can help develop targeted interventions to improve emotion recognition and emotion regulation for PLHIV.

Facial emotion recognition (FER) is the technology or process of identifying and interpreting human emotions based on the analysis of facial expressions. It involves using computer algorithms and machine learning techniques to detect and classify emotional states from images or videos of human faces. Further, FER plays a vital role in recognizing and understanding human emotions to better interpret someone’s feelings, intentions, and attitudes. In the present time, it is widely used in various fields such as healthcare, human–computer interaction, law enforcement, security, and beyond. FER has a wide range of practical applications across various industries including Emotion Monitoring, Adaptive Learning, and Virtual Assistants. This paper presents a comparative analysis of FER algorithms, focusing on deep learning approaches. The performance of different datasets, including FER2013, JAFFE, AffectNet, and Cohn–Kanade, is evaluated using convolutional neural networks (CNNs), deep face, attentional convolutional networks (ACNs), and deep belief networks (DBNs). Among the tested algorithms, DBNs outperformed other algorithms, reaching the highest accuracy of 98.82%. These results emphasize the effectiveness of deep learning techniques, particularly DBNs, in FER. Additionally, outlining the advantages and disadvantages of current research on facial emotion identification might direct future research efforts in the direction of the most profitable directions.

INTRODUCTION: As one of the important research directions in the field of computer vision, facial emotion recognition plays an important role in people's daily life. How to make the computer accurately read facial emotion is an important research content.OBJECTIVES: In the current research on facial emotion recognition, there are some problems such as poor generalization ability of network model and low robustness of recognition system. To solve above problems, we propose a novel facial emotion recognition method.METHODS: Our method of feature extraction using the stationary wavelet entropy, which combines single hidden layer feedforward neural network with biogeography-based optimization for facial emotion recognition.RESULTS: The simulation results show that the overall accuracy of our method is 93.79±1.24%.CONCLUSION: This model is superior to the current mainstream facial emotion recognition models in the performance of facial emotion detection. In future research, we will try deep learning and other training methods.

Objective: Clozapine and risperidone are used for treatment-resistant schizophrenia and known to improve the positive and negative symptoms. However, there are some conflicts about effects on social cognition, which is measured with facial emotion recognition ability. The impairments in facial emotion recognition ability have frequently been in different stages of the illness and might have negative influences on psychosocial functioning. In the present study, we aimed to examine clozapine and risperidone effects recognizing facial emotions in patient with treatment-resistant schizophrenia.Methods: Thirty-four patients were screened for the study, and 19 patients were included. All patients were evaluated with Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale for Schizophrenia, and Functional Remission of General Schizophrenia Scale at baseline and after 16–20 weeks of clozapine (n = 12) or risperidone (n = 7) treatment. Furthermore, the Facial Emotion Recognition Test was performed before and after treatment. The test included the photos of four male and four female models (totally 56 mixed photos) with happy, surprised, fearful, sad, angry, disgusted, and neutral facial expressions from Ekman and Friesen’s catalog.Results: The mean dose of the index drug in clozapine group was 295.83 ± 103.26 mg/day. The mean positive (p = .002), negative (p = .050) general psychopathology (p = .002), and total score (p = .002) according to the PANSS were significantly improved after treatment. The mean dose of the index drug in risperidone group was 6.86 ± 1.57 mg/day. The mean positive symptom (p = .018) and total score (p = .041) were significantly improved after treatment but negative symptom scale (p = .396) and general psychopathology (p = .149) scores did not change. There were no significant differences between baseline and after treatment in clozapine and risperidone group according to the accuracy rate of facial emotion recognition expressions (p > .05 for each). At baseline phase, the patients were significantly impaired in recognizing disgusted faces in risperidone than those in clozapine group (p = .032) and it was significantly poorer after treatment with risperidone than with clozapine (p = .031). The patients responded significantly faster after the treatment to all facial emotions except for fearful faces (p = .355).Conclusions: Clozapine and risperidone were not found to have extensive effects on the ability to recognize facial emotions because of ineffectiveness to negative symptoms as in our study. We speculated that the higher dopaminergic receptor occupancy rate of risperidone in insular cortex than that of clozapine might be related with hypo-activation of insula that was associated with particular deficit in ability to recognize expressions of disgust in patients with schizophrenia. Impaired facial emotion recognition ability is present even in first-episode psychosis, which might be a trait marker in schizophrenia.

Facial expression recognition via ResNet-50