• Korean Journal of Cognitive Science
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• v.16 no.4
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• pp.323-331
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• 2005

This paper proposes an improved system for recognizing facial expressions in various internal states that is illumination-invariant and without detectable rue such as a neutral expression. As a preprocessing to extract the facial expression information, a whitening step was applied. The whitening step indicates that the mean of the images is set to zero and the variances are equalized as unit variances, which reduces murk of the variability due to lightening. After the whitening step, we used the facial expression information based on principal component analysis(PCA) representation excluded the first 1 principle component. Therefore, it is possible to extract the features in the lariat expression images without detectable cue of neutral expression from the experimental results, we ran also implement the various and natural facial expression recognition because we perform the facial expression recognition based on dimension model of internal states on the images selected randomly in the various facial expression images corresponding to 83 internal emotional states.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.23 no.8
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• pp.37-43
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• 2018

In this paper, we propose a framework that automatically suggests emotion using emotion analysis method based on facial expression change. We use Microsoft's Emotion API to calculate and analyze emotion values in facial expressions to recognize emotions that change over time. In this step, we use standard deviations based on peak analysis to measure and classify emotional changes. The difference between the classified emotion and the normal emotion is calculated, and the difference is used to recognize the emotion abnormality. We match user's emotions to relatively relaxed emotions using histograms and emotional meshes. As a result, we provide relaxed emotions to users through images. The proposed framework helps users to recognize emotional changes easily and to train their emotions through emotional relaxation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3473-3487
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• 2020

In this paper, we present a new approach to creating speech animation with emotional expressions using a small set of example models. To generate realistic facial animation, two example models called key visemes and expressions are used for lip-synchronization and facial expressions, respectively. The key visemes represent lip shapes of phonemes such as vowels and consonants while the key expressions represent basic emotions of a face. Our approach utilizes a text-to-speech (TTS) system to create a phonetic transcript for the speech animation. Based on a phonetic transcript, a sequence of speech animation is synthesized by interpolating the corresponding sequence of key visemes. Using an input parameter vector, the key expressions are blended by a method of scattered data interpolation. During the synthesizing process, an importance-based scheme is introduced to combine both lip-synchronization and facial expressions into one animation sequence in real time (over 120Hz). The proposed approach can be applied to diverse types of digital content and applications that use facial animation with high accuracy (over 90%) in speech recognition.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.26-34
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• 2024

Most human emotions are conveyed through facial expressions, which represent the predominant source of emotional data. This research investigates the impact of crowds on human emotions by analysing facial expressions. It examines how crowd behaviour, face recognition technology, and deep learning algorithms contribute to understanding the emotional change according to different level of crowd. The study identifies common emotions expressed during congestion, differences between crowded and less crowded areas, changes in facial expressions over time. The findings can inform urban planning and crowd event management by providing insights for developing coping mechanisms for affected individuals. However, limitations and challenges in using reliable facial expression analysis are also discussed, including age and context-related differences.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.61-71
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• 2019

Due to the limit of the time length of advertisement, facial expressions among the types of nonverbal communication are much more expressive and convincing to appeal to costumers. The purpose of this paper is not only to investigate how facial expressions are portrayed but also to examine how facial expressions convey emotion in TV advertisements. Research subjects are TV advertisements of and which had the wide range of popularity for customer known as one of the most touching commercials. The research method is Facial Action Coding System based on the theoretical perspective of a discrete emotions and designed to measure specific facial muscle movements. This research is to analyse the implications of facial expressions in the both TV ads by using FACS based on Psychology as well as anatomy. From the all the result of this, it is shown that the facial expressions portrayed with the conflict of emotional states and the dramatic emotional relief of the heroin could move more customers' emotions.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.105-129
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• 2004

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.562-567
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• 2009

As a key mechanism of the human emotion interaction, Facial Expression is a powerful tools in HRI(Human Robot Interface) such as Human Computer Interface. By using a facial expression, we can bring out various reaction correspond to emotional state of user in HCI(Human Computer Interaction). Also it can infer that suitable services to supply user from service agents such as intelligent robot. In this article, We addresses the issue of expressive face modeling using an advanced active appearance model for facial emotion recognition. We consider the six universal emotional categories that are defined by Ekman. In human face, emotions are most widely represented with eyes and mouth expression. If we want to recognize the human's emotion from this facial image, we need to extract feature points such as Action Unit(AU) of Ekman. Active Appearance Model (AAM) is one of the commonly used methods for facial feature extraction and it can be applied to construct AU. Regarding the traditional AAM depends on the setting of the initial parameters of the model and this paper introduces a facial emotion recognizing method based on which is combined Advanced AAM with Bayesian Network. Firstly, we obtain the reconstructive parameters of the new gray-scale image by sample-based learning and use them to reconstruct the shape and texture of the new image and calculate the initial parameters of the AAM by the reconstructed facial model. Then reduce the distance error between the model and the target contour by adjusting the parameters of the model. Finally get the model which is matched with the facial feature outline after several iterations and use them to recognize the facial emotion by using Bayesian Network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.196-212
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• 2014

Facial expression recognition is an important part in emotional interaction between human and machine. This paper proposes a facial expression recognition approach based on multi-classifier fusion with stacking algorithm. The kappa-error diagram is employed in base-level classifiers selection, which gains insights about which individual classifier has the better recognition performance and how diverse among them to help improve the recognition accuracy rate by fusing the complementary functions. In order to avoid the influence of the chance factor caused by guessing in algorithm evaluation and get more reliable awareness of algorithm performance, kappa and informedness besides accuracy are utilized as measure criteria in the comparison experiments. To verify the effectiveness of our approach, two public databases are used in the experiments. The experiment results show that compared with individual classifier and two other typical ensemble methods, our proposed stacked ensemble system does recognize facial expression more accurately with less standard deviation. It overcomes the individual classifier's bias and achieves more reliable recognition results.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.9-19
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• 2012

Virtual human used as HCI in digital contents expresses his various emotions across modalities like facial expression and body posture. However, few studies considered combinations of such nonverbal multimodal in emotion perception. Computational engine models have to consider how a combination of nonverbal modal like facial expression and body posture will be perceived by users to implement emotional virtual human, This paper proposes the impacts of nonverbal multimodal in design of emotion expressed virtual human. First, the relative impacts are analysed between different modals by exploring emotion recognition of modalities for virtual human. Then, experiment evaluates the contribution of the facial and postural congruent expressions to recognize basic emotion categories, as well as the valence and activation dimensions. Measurements are carried out to the impact of incongruent expressions of multimodal on the recognition of superposed emotions which are known to be frequent in everyday life. Experimental results show that the congruence of facial and postural expression of virtual human facilitates perception of emotion categories and categorical recognition is influenced by the facial expression modality, furthermore, postural modality are preferred to establish a judgement about level of activation dimension. These results will be used to implementation of animation engine system and behavior syncronization for emotion expressed virtual human.