• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.754-759
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• 2007

In this paper, we proposed the Bi-Modal Sensor Fusion Algorithm which is the emotional recognition method that be able to classify 4 emotions (Happy, Sad, Angry, Surprise) by using facial image and speech signal together. We extract the feature vectors from speech signal using acoustic feature without language feature and classify emotional pattern using Neural-Network. We also make the feature selection of mouth, eyes and eyebrows from facial image. and extracted feature vectors that apply to Principal Component Analysis(PCA) remakes low dimension feature vector. So we proposed method to fused into result value of emotion recognition by using facial image and speech.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.18-23
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• 2005

It is difficult to define and classify human emotion. These vague human emotion appear not in single emotion, but in combination of various emotion. And among them, a remarkable emotion is expressed. This paper proposes a emotional expression algorithm using dynamic emotion space, which give facial expression in similar with vague human emotion. While existing avatar express several predefined emotions from database, our emotion expression system can give unlimited various facial expression by expressing emotion based on dynamically changed emotion space. In order to see whether our system practically give complex and various human expression, we perform real implementation and experiment and verify the efficacy of emotional expression system based on dynamic emotion space.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.361-368
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• 2017

This study analyzes into the characteristic appearance in distintion with emotional changes toward visual forms of psychology along with character development in the 3D animation industry. In this regard, the study seeks to propose essential targets of the five emotional characters from the Pixar's animation Inside-Out to prove psychological effects to the character's visual appearance. As a previous research, the study analysis the visual representations oriented toward both emotional facial expression and emotional color expression using both Paul Ekman and Robert Plutchik's human basic emotion research. The purpose of this study is to present the visual guideline of emotional character's appearance through the various human expression for differentiated character development in animation production.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.267-272
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• 2009

Video digests provide an effective way of confirming a video content rapidly due to their very compact form. By watching a digest, users can easily check whether a specific content is worth seeing in full. The impression created by the digest greatly influences the user's choice in selecting video contents. We propose a novel method of automatic digest creation that evokes a joyful impression through the created digest by exploiting smile/laughter facial expressions as emotional cues of joy from video. We assume that a digest presenting smiling/laughing faces appeals to the user since he/she is assured that the smile/laughter expression is caused by joyful events inside the video. For detecting smile/laughter faces we have developed a neural network based method for classifying facial expressions. Video segmentation is performed by automatic shot detection. For creating joyful digests, appropriate shots are automatically selected by shot ranking based on the smile/laughter detection result. We report the results of user trials conducted for assessing the visual impression with automatically created 'joyful' digests produced by our system. The results show that users tend to prefer emotional digests containing laughter faces. This result suggests that the attractiveness of automatically created video digests can be improved by extracting emotional cues of the contents through automatic facial expression analysis as proposed in this paper.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.834-839
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• 2009

It is a key element that the problem of emotional feature extraction based on facial image to recognize a human emotion status. In this paper, we propose an Advanced AAM that is improved version of proposed Facial Expression Recognition Systems based on Bayesian Network by using FACS and AAM. This is a study about the most efficient method of optimal facial feature area for human emotion recognition about random user based on generalized HCI system environments. In order to perform such processes, we use a Statistical Shape Analysis at the normalized input image by using Advanced AAM and FACS as a facial expression and emotion status analysis program. And we study about the automatical emotional feature extraction about random user.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.313-323
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• 2019

For the actors on video, facial expression acting can easily become 'forced facial expression' or 'over-acting'. Also, if self-restraint is emphasized too much, then it becomes 'flat acting' with insufficient emotions. By bringing forth questions in regard to such facial expression acting methods, this study analyzed the facial expression acting of the actors in genre dramas with strong commercial aspects. In conclusion, the facial expression acting methods of the actors in genre dramas were being conducted in a typical way. This means that in visual conventions of video acting, the aesthetic standard has become the important standard in the facial expression acting of the actors. In genre dramas, the emotions of the characters are often revealed in close-up shots. Within the close-up shot, the most important expressive medium in a 'zoomed-in face' is the 'pupil of the eye', and emotions are mostly expressed through the movements of the eye and muscles around it. The second most important expressive medium is the 'mouth'. The differences in the degree of opening and closing the mouth convey diverse emotions along with the expression of the 'eye'. In addition, tensions in the facial muscles greatly hinder the expression of emotions, and the movement of facial muscles must be minimized to prevent excessive wrinkles from forming on the surface of the face. Facial expressions are not completed just with the movement of the muscles. Ultimately, the movement of the muscle is the result of emotions. Facial expression acting takes place after having emotional feelings. For this, the actor needs to go through the process of 'personalization' of a character, such as 'emotional memory', 'concentration' and 'relaxation' which are psychological acting techniques of Stanislavsky. Also, the characteristics of close-up shots that visually reveal the 'inner world' should be recognized. In addition, it was discovered that the facial expression acting is the reaction acting that provides the important point in the unfolding of narratives, and that the method of facial expression and the size of the shots required for the actors are different depending on the roles of main and supporting characters.

• The Journal of Korea Robotics Society
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• v.1 no.1
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• pp.58-65
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• 2006

The emotional interface is essential technology for the robot to provide the proper service to the user. In this research, we developed emotional components for the service robot such as a neural network based facial expression recognizer, emotion expression technologies based on 3D graphical face expression and joints movements, considering a user's reaction, behavior selection technology for emotion expression. We used our humanoid robots, AMI and AMIET as the test-beds of our emotional interface. We researched on the emotional interaction between a service robot and a user by integrating the developed technologies. Emotional interface technology for the service robot, enhance the performance of friendly interaction to the service robot, to increase the diversity of the service and the value-added of the robot for human. and it elevates the market growth and also contribute to the popularization of the robot. The emotional interface technology can enhance the performance of friendly interaction of the service robot. This technology can also increase the diversity of the service and the value-added of the robot for human. and it can elevate the market growth and also contribute to the popularization of the robot.

• Korean Journal of Child Studies
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• v.24 no.6
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• pp.15-31
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• 2003

This study examined the effects of emotion inducing contexts on types of infants smiling. Facial expressions of forty-five 11-to 15-month-old infants were videotaped in an experimental lab with positive and negative emotional contests. Infants' smiling was identified as the Duchenne smile or non-Duchenne smile based on FACS(Facial Action Coding System, Ekman & Friesen, 1978). Duration of smiling types was analyzed. Overall, infants showed more smiling in the positive than in the negative emotional context. Occurrence of Duchenne smiling was more likely in the positive than in the negative context and in the peek-a-boo than in the melody toy condition within the same positive context. Non-Duchenne smiling did not differ by context.

• KNOM Review
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• v.24 no.2
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• pp.35-47
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• 2021

Various stress exists in a modern society, which requires constant competition and improvement. A person under stress often shows his pressure in his facial expression and language. Therefore, it is possible to measure the pressure using facial expression and language analysis. The paper proposes a stress measurement system using facial expression and language sensitivity analysis. The method analyzes the person's facial expression and language sensibility to derive the stress index based on the main emotional value and derives the integrated stress index based on the consistency of facial expression and language. The quantification and generalization of stress measurement enables many researchers to evaluate the stress index objectively in general.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.523-528
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• 2015

This paper proposes an algorithm for risk situation recognition using facial expression. The proposed method recognitions the surprise and fear expression among human's various emotional expression for recognizing risk situation. The proposed method firstly extracts the facial region from input, detects eye region and lip region from the extracted face. And then, the method applies Uniform LBP to each region, discriminates facial expression, and recognizes risk situation. The proposed method is evaluated for Cohn-Kanade database image to recognize facial expression. The DB has 6 kinds of facial expressions of human being that are basic facial expressions such as smile, sadness, surprise, anger, disgust, and fear expression. The proposed method produces good results of facial expression and discriminates risk situation well.