• The Journal of Korean Institute for Practical Engineering Education
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• v.4 no.2
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• pp.171-175
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• 2012

Recently, the research on ICT (Information and Communication Technology), which cognizes and communicates human's emotion through information technology, is increasing. For instance, there are researches on phones and services that perceive users' emotions through detecting people's voices, facial emotions, and biometric data. In short, emotions which were used to be predicted only by humans are, now, predicted by digital equipment instead. Among many ICT researches, research on emotion recognition from face is fully expected as the most effective and natural human interface. This paper studies about sensitivity ICT and examines mechanism of facial expression recognition system as an example of sensitivity ICT.

• The Journal of the Korea Contents Association
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• v.16 no.5
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• pp.21-34
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• 2016

In the design and develop a virtual character, it is important to correctly deliver target emotions generated by the combination of facial expression and gesture. The purpose of this study is to examine the effect of congruent/incongruent between gesture and facial expression on target emotion. In this study four emotions, joy, sadness, fear, and anger, are applied. The results of study showed that sadness emotion were incorrectly perceived. Moreover, it was perceived as anger instead of sadness. Sadness can be easily confused when facial expression and gestures were simultaneously presented. However, in the other emotional status, the intended emotional expressions were correctly perceived. The overall evaluation of virtual character's emotional expression was significantly low when joy gesture was combined with sad facial expression. The results of this study suggested that emotional gesture is more influential correctly to deliver target emotions to users. This study suggested that social cues like gender or age of virtual character should be further studied.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.24 no.2
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• pp.83-89
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• 2013

Objectives : It is known that children with attention-deficit hyperactivity disorder (ADHD) experience significant difficulty in recognizing facial emotion, which involves processing of emotional facial expressions rather than speech, compared to children without ADHD. This objective of this study is to investigate the differences in facial emotion recognition between children with ADHD and normal children used as control. Methods : The children for our study were recruited from the Suwon Project, a cohort comprising a non-random convenience sample of 117 nine-year-old ethnic Koreans. The parents of the study participants completed study questionnaires such as the Korean version of Child Behavior Checklist, ADHD Rating Scale, Kiddie-Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version. Facial Expression Recognition Test of the Emotion Recognition Test was used for the evaluation of facial emotion recognition and ADHD Rating Scale was used for the assessment of ADHD. Results : ADHD children (N=10) were found to have impaired recognition when it comes to Emotional Differentiation and Contextual Understanding compared with normal controls (N=24). We found no statistically significant difference in the recognition of positive facial emotions (happy and surprise) and negative facial emotions (anger, sadness, disgust and fear) between the children with ADHD and normal children. Conclusion : The results of our study suggested that facial emotion recognition may be closely associated with ADHD, after controlling for covariates, although more research is needed.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.754-771
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• 2021

In the task of continuous dimension emotion recognition, the parts that highlight the emotional expression are not the same in each mode, and the influences of different modes on the emotional state is also different. Therefore, this paper studies the fusion of the two most important modes in emotional recognition (voice and visual expression), and proposes a two-mode dual-modal emotion recognition method combined with the attention mechanism of the improved AlexNet network. After a simple preprocessing of the audio signal and the video signal, respectively, the first step is to use the prior knowledge to realize the extraction of audio characteristics. Then, facial expression features are extracted by the improved AlexNet network. Finally, the multimodal attention mechanism is used to fuse facial expression features and audio features, and the improved loss function is used to optimize the modal missing problem, so as to improve the robustness of the model and the performance of emotion recognition. The experimental results show that the concordance coefficient of the proposed model in the two dimensions of arousal and valence (concordance correlation coefficient) were 0.729 and 0.718, respectively, which are superior to several comparative algorithms.

• Science of Emotion and Sensibility
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• v.16 no.3
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• pp.281-292
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• 2013

The purpose of this study was to identify the effect of gesture and facial expression on persona effects. Fifty-six college students were recruited for this study, and non-verbal communication skills were applied to a pedagogical agent with gesture (conversational vs. deictic) and facial expression. The conversational gesture may have relationship with social interaction hypothesis of pedagogical agent while the deictic gesture may have relationship with attentional guidance hypothesis. The facial expression can be assumed to facilitate the social interaction between the pedagogical agent and learners. Interestingly, the conversational gesture group showed a tendency of outperforming the deictic gesture group. It may imply that the social interaction theory has a strong impact on cognitive support as well as social interaction for learners. There was a significant interaction effect on the engagement when both of facial expression and conversational gesture were applied. This result has two implications. First, facial expression can facilitate the persona effect for engagement.

• Journal of KIISE:Software and Applications
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• v.27 no.8
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• pp.827-833
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• 2000

Facial expression is an effective tool to express human emotion. In this paper, a facial expression analysis method based on the base faces and their blending ratio is proposed. The seven base faces were chosen as axes describing and analyzing arbitrary facial expression. We set up seven facial expressions such as, surprise, fear, anger, disgust, happiness, sadness, and expressionless as base faces. Facial expression was built by fitting generic 3D facial model to facial image. Two comparable methods, Genetic Algorithms and Simulated Annealing were used to search the blending ratio of base faces. The usefulness of the proposed method for facial expression analysis was proved by the facial expression synthesis results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.155-161
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• 2024

In this paper, we propose a new model by adding landmark information as a feature vector to the existing CNN-based facial emotion classification model. Facial emotion classification research using CNN-based models is being studied in various ways, but the recognition rate is very low. In order to improve the CNN-based models, we propose algorithms that improves facial expression classification accuracy by combining the CNN model with a landmark-based fully connected network obtained by ASM. By including landmarks in the CNN model, the recognition rate was improved by several percent, and experiments confirmed that further improved results could be obtained by adding FACS-based action units to the landmarks.

• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.799-807
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• 2004

In this paper, we deal with an emotion recognition method using facial images and speech signal. Six basic human emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. Emotion recognition using the facial expression is performed by using a multi-resolution analysis based on the discrete wavelet transform. And then, the feature vectors are extracted from the linear discriminant analysis method. On the other hand, the emotion recognition from speech signal method has a structure of performing the recognition algorithm independently for each wavelet subband and then the final recognition is obtained from a multi-decision making scheme.

• Korean Journal of Cognitive Science
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• v.18 no.1
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• pp.57-68
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• 2007

This study explores children's categories of emotion understanding from facial expressions onto two dimensional structure of emotion. Children of 89 from 3 to 5 years old were required to those facial expressions related the fourteen emotion terms. Facial expressions applied for experiment are used the photographs rated the degree of expression in each of the two dimensions (pleasure-displeasure dimension and arousal-sleep dimension) on a nine-point scale from 54 university students. The experimental results showed that children indicated the greater stability in arousal dimension than stability in pleasure-displeasure dimension. Emotions about sadness, sleepiness, anger and surprise onto two dimensions was understand very well, but emotions about fear, boredom were showed instability in pleasure-displeasure dimension. Specifically, 3 years old children indicated highly the perception in a degree of arousal-sleep than perception of pleasure-displeasure.

• Proceedings of the Korea Society of Design Studies Conference
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• 2005.10a
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• pp.32-33
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• 2005