Facial emotion recognition in adopted children

Paine, Amy L.; Goozen, Stephanie Helena Maria Van; Burley, Daniel; Anthony, Rebecca; Shelton, Katherine Helen

doi:10.1007/s00787-021-01829-z

“…The Strengths and Difficulties Questionnaire (SDQ) is a 25-item screening tool [ 33 ], which is widely used to assess different psychosocial and behavioral problems in children. [ 34 , 35 , 36 , 37 ] The SDQ contains five subscales containing five items each: emotional symptoms, conduct problems, hyperactivity, peer problems and prosocial behavior. Higher scores indicate more significant degrees of behavioral and emotional problems, except for prosocial behavior, for which a higher score indicates fewer behavioral problems.…”

Section: Methodsmentioning

confidence: 99%

Facial Emotion Recognition Deficit in Children with Moderate/Severe Obstructive Sleep Apnea

Fu-jun

¹

,

Chen

²

,

Wu

³

et al. 2022

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Although previous studies have reported a facial expression classification deficit among adults with SDB, we do not know whether these findings can be generalized to children. In our study, children with sleep-disordered breathing (SDB) were divided into three groups: primary snoring (n = 51), mild obstructive sleep apnea (OSA) (n = 39), and moderate/severe OSA (n = 26). All participants, including 20 healthy controls, underwent an overnight polysomnography recording and the Emotional Expression Recognition Task. Psychosocial problems were evaluated using the parent-reported Strengths and Difficulties Questionnaire (SDQ). There was a borderline significant interaction between expression category and group on reaction times. Further analysis revealed that positive classification advantage (PCA) disappeared in the moderate/severe OSA group, whereas it persisted in the control, primary snoring, and mild OSA groups. Emotional symptoms were positively correlated with OAHI. In both the happy and sad conditions, RT was negatively related to age and body mass index (BMI) but was independent of the obstructive apnea-hypopnea index (OAHI), arterial oxygen (SaO2) and total sleep time. The accuracy of identifying a sad expression was negatively related to conduct problems. Children with moderate/severe OSA exhibited dysfunction in facial expression categorization, which could potentially affect social communication ability.

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“…Besides, not being a permanent adoption, actual guardians might not show their own true emotions to these LBC in some cases. Adverse family life experiences in early childhood would create a disadvantage in emotional comprehension, such as facial emotion understanding [62,63]. Even if only one parent left home, another parent could not make up for the emotional lack young children experience [64].…”

Section: Family Emotional Function Would Make Sense In Lbc's Eumentioning

confidence: 99%

The Development of Emotion Understanding among Five- and Six-Year-Old Left-Behind Children in Rural China

Tan

¹

,

Fang

²

,

Chen

³

2023

IJERPH

0

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The left-behind children (LBC), separated from their mother/father or parents for a long period of time, have long been discussed as a subject of concern in China. Existing research has concluded that rural children who did not migrate with parents are subject to emotional risks. In the present study, the purpose is to study the impact of parental migration on early emotional understanding. Purposeful sampling was used to recruit 180 children aged five to six years in rural areas of Guangdong province, including LBC and non-left-behind children (NLBC). Their level of emotional understanding (EU) was assessed by the emotional comprehension test (TEC) adapted to the Chinese context. The results showed that, on the three levels (External, Internal, Reflective) of emotional understanding, LBC aged five- to six- years old scored significantly lower than NLBC as counterparts. On the whole, the emotional comprehension ability of preschool LBC was significantly lower than that of NLBC. However, there were no significant differences within LBC nurtured by single parents, grandparents, and other relatives. This study confirmed that parental migration in early childhood considerably impacted rural LBC’s emotional understanding and affectional adjustment, which provided a significant basis for increasing parental care and early childhood companionship in rural areas.

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“…According to the data analysis results in the previous section, we will continue to optimize the calculation process of Algorithm 2 to gradually reduce the final detection difference [55]. At present, for the new Algorithm 3, we obtain logarithmic values and output them based on the original output through integral weighted square summation, which further reduces the situation that the target emotion is too different in the detection process.…”

Section: Algorithm Improvement Testmentioning

confidence: 99%

Implementation of Artificial Intelligence Image Emotion Detection Mechanism Based on Python Architecture for Industry 4.0

Jinnuo

¹

,

Goyal

²

,

Tesfayohanis³

et al. 2022

Journal of Nanomaterials

7

0

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Since the beginning of the 21st century, with the development of information technology, researchers in various fields have gradually increased their research on human emotion and behavior. The current research mechanism used in emotion and behavior research is artificial intelligence technology. Through the literature survey and data analysis in related fields, it is found that the acquisition of human emotions and behaviors will be carried out through facial feature algorithm for point capture and combined with machine learning for output detection and analysis. Among them, the detection process requires machine learning of artificial intelligence first. This paper firstly analyzes and summarizes the advantages of Python programs at this stage and completes the preliminary work of system construction by setting and installing platform parameters. In the research process, this paper uses the existing algorithm to apply the σ E value algorithm to the samples and conducts preliminary tests. The overall detection values in the test data are relatively average, and there are still differences in the samples. At the same time, we compare the U E and T E detection algorithms according to the output Y value of the algorithm in the machine learning. The detection rate of some emoticons in the U E algorithm is high, but the detection rate of other emoticons is low. Finally, according to the limitation of the output method in the mathematical formula, a new algorithm σ x of taking the weighted sum and taking the logarithm and then taking the square root is proposed again. According to the statistical analysis, the overall average value of the final algorithm has been improved, and the overall detection rate is about 80%; compared with the T E and U E algorithms, the overall detection frequency fluctuates less. The σ x algorithm in the frequency fluctuation data table in the paper is also superior to the existing algorithms in machine learning, sample testing, and data in the frequency fluctuation. Our next direction will be to use the Python main program to perform AI automatic facial emotion detection work by combining the new algorithm σ x with the V value, DWT, and CNN algorithm in the facial recognition feature through machine learning.

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Facial emotion recognition in adopted children

Cited by 8 publications

References 85 publications

Facial Emotion Recognition Deficit in Children with Moderate/Severe Obstructive Sleep Apnea

Facial Emotion Recognition Deficit in Children with Moderate/Severe Obstructive Sleep Apnea

The Development of Emotion Understanding among Five- and Six-Year-Old Left-Behind Children in Rural China

Implementation of Artificial Intelligence Image Emotion Detection Mechanism Based on Python Architecture for Industry 4.0

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