• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.748-757
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• 2017

In order to recognize the facial expressions, good features that can express the facial expressions are essential. It is also essential to find the characteristic areas where facial expressions appear discriminatively. In this study, we propose a directional LBP feature for facial expression recognition and a method of finding directional LBP operation and feature region for facial expression classification. The proposed directional LBP features to characterize facial fine micro-patterns are defined by LBP operation factors (direction and size of operation mask) and feature regions through AdaBoost learning. The facial expression classifier is implemented as a SVM classifier based on learned discriminant region and directional LBP operation factors. In order to verify the validity of the proposed method, facial expression recognition performance was measured in terms of accuracy, sensitivity, and specificity. Experimental results show that the proposed directional LBP and its learning method are useful for facial expression recognition.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.203-215
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• 2023

Social robots, which are mainly used by individuals, emphasize the importance of human-robot relationships (HRR) more compared to other types of robots. Emotional expression in robots is one of the key factors that imbue HRR with value; emotions are mainly expressed through the face. However, because of cultural and preference differences, the desired robot facial expressions differ subtly depending on the user. It was expected that a robot facial expression customization tool may mitigate such difficulties and consequently improve HRR. To prove this, we created a robot facial expression customization tool and a prototype robot. We implemented a suitable emotion engine for generating robot facial expressions in a dynamic human-robot interaction setting. We conducted experiments and the users agreed that the availability of a customized version of the robot has a more positive effect on HRR than a predefined version of the robot. Moreover, we suggest recommendations for future improvements of the customization process of robot facial expression.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.34-37
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• 2007

Face recognition is still a challenging problem in pattern recognition field which is affected by different factors such as facial expression, illumination, pose etc. The facial feature such as eyes, nose, and mouth constitute a complete face. Mouth feature of face is under the undesirable effect of facial expression as many factors contribute the low performance. We proposed a new approach for face recognition under facial expression applying two cascaded classifiers to improve recognition rate. All facial expression images are treated by general purpose classifier at first stage. All rejected images (applying threshold) are used for adaptation using GA for improvement in recognition rate. We apply Gabor Wavelet as a general classifier and Gabor wavelet with Genetic Algorithm for adaptation under expression variance to solve this issue. We have designed, implemented and demonstrated our proposed approach addressing this issue. FERET face image dataset have been chosen for training and testing and we have achieved a very good success.

• Journal of Korea Multimedia Society
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• v.25 no.7
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• pp.964-972
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• 2022

The Uncanny Valley phenomenon has already been deemed as theoretical, and the characteristics of game character expression elements for the Uncanny Valley phenomenon were recognized through case analysis as well. By theoretical consideration and case studies, it was found out that the influential elements of the Uncanny Valley phenomenon can be classified as two primary factors: character surface treatment and facial expression animation. The prepared experimental materials and adjectives were measured to be Five-Point Likert Scale. The measured results were evaluated for both influence and comparative analysis through essential statistical analysis and Repeated Measuring ANOVA in SPSS. The conclusions which were drawn from this research are as follows: The surface treatment of characters did not substantially affect the Uncanny Valley phenomenon. Instead, character's expression animation had a significant impact on the Uncanny Valley phenomenon, which also led to another conclusion that the facial expression animation had an overall deeper impact on Uncanny Valley phenomenon compared with character's surface treatment. It was the unnatural facial expression animation that controlled all of the independent variables and also caused the Uncanny Valley phenomenon. In order for game characters to evade the Uncanny Valley phenomenon and enhance game immersion, the facial expression animation of the character must be done spontaneously.

• Proceedings of the Korea Contents Association Conference
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• 2006.05a
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• pp.160-163
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• 2006

If an instruction system understood the interest and activeness of a learner in real time, it could provide some interesting factors when a learner is tired of learning. It could work as an adaptive tutoring system to help a learner to understand something difficult to understand. Currently the area of the facial expression recognition mainly deals with the facial expression of adults focusing on anger, hatred, fear, sadness, surprising and gladness. These daily facial expressions couldn't be one of expressions of a learner in e-Learning. They should first study the facial expressions of a learner in e-Learning to recognize the feeling of a learner. Collecting as many expression pictures as possible, they should study the meaning of each expression. This study, as a prior research, analyzes the feelings of learners and facial expressions of learners in e-Learning in relation to the feelings to establish the facial expressions database.

• Journal of the Korean Society of Fashion and Beauty
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• v.3 no.2 s.2
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• pp.31-38
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• 2005

Make-up changes facial images. In particular, eyebrow make-up is a part of changing expression most easily and effectively. While color make-up is helpful to produce women's desired image with their favorite colors, eyebrow make-up is hidden actor to give a clear impression to others. Therefore, this study connected facial type which is an important factor deciding facial image with eyebrow, examined image of eyebrow make-up and that changed by facial types and aimed to be helpful in producing more effective facial image with eyebrow make-up considering one's facial type. Consequently, it was found that eyebrow make-up was a great factor in making better facial impression and image and complementing the weakness of facial type. h strong impression of facial type can be changed into soft shape or foolish shape in worse case depending on the types of eyebrow make-up. Eyebrow make-up shows charming image as angle of eyebrow is steep, heavy image as eyebrow is horizontal, cold image as eyebrow tail rises and simple and dull image as it lowers. Therefore, it is known that image of eyebrow make-up can be governed by several factors including angle and direction of eyebrow. Consequently, it is thought that most effective eyebrow make-up considers individual facial types, images of their eyes, noses and mouths and factors deciding angle, direction and colors of eyebrow.

• Science of Emotion and Sensibility
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• v.17 no.4
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• pp.61-70
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• 2014

Verbal communication has limits in the interaction between robot and man, and therefore nonverbal communication is required for realizing smoother and more efficient communication and even the emotional expression of the robot. This study derived 7 pieces of nonverbal information based on shopping behavior using the robot designed to support shopping, selected facial expression as the element of the nonverbal information derived, and coded face components through 2D analysis. Also, this study analyzed the significance of the expression of nonverbal information using 3D animation that combines the codes of face components. The analysis showed that the proposed expression method for nonverbal information manifested high level of significance, suggesting the potential of this study as the base line data for the research on nonverbal information. However, the case of 'embarrassment' showed limits in applying the coded face components to shape and requires more systematic studies.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.748-750
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• 2023

Facial expression recognition technology's rapid growth and development have garnered significant attention in recent years. This technology holds immense potential for various applications, making it crucial to stay up-to-date with the latest trends and advancements. Simultaneously, it is essential to identify and address the challenges that impede the technology's progress. Motivated by these factors, this study aims to understand the latest trends, future directions, and challenges in facial expression recognition technology by utilizing text mining to analyze papers published between 2020 and 2023. Our research focuses on discerning which aspects of these papers provide valuable insights into the field's recent developments and issues. By doing so, we aim to present the information in an accessible and engaging manner for readers, enabling them to understand the current state and future potential of facial expression recognition technology. Ultimately, our study seeks to contribute to the ongoing dialogue and facilitate further advancements in this rapidly evolving field.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.113-117
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• 2018

Human emotions are revealed by various factors. Words, actions, facial expressions, attire and so on. But people know how to hide their feelings. So we can not easily guess its sensitivity using one factor. We decided to pay attention to behaviors and facial expressions in order to solve these problems. Behavior and facial expression can not be easily concealed without constant effort and training. In this paper, we propose an algorithm to estimate human emotion through combination of two results by gradually learning human behavior and facial expression with little data through the deep learning method. Through this algorithm, we can more comprehensively grasp human emotions.

• The Journal of the Korea Contents Association
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• v.5 no.2
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• pp.137-145
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• 2005

This study is a model of research for the developing 3D character contents about facial expression as a sort of non-linguistic signs, focusing on an expression of emotion factors of a person. It contributes a framework for symbolic analysis about Human's emotions along with a general review of expression. The human face is the most complex and versatile of all species. For humans, the face is a ich and versatile instrument serving many different functions. It serves as a window to display one's own motivational state. This makes one's behavior more predictable and understandable to others and improves communication. The face can be used to supplement verbal communication. A prompt facial display can reveal the speaker's attitude about the information being conveyed. Alternatively, the face can be used to complement verbal communication, such as lifting of eyebrows to lend additional emphasis to stressed word. The facial expression plays a important role under the digital visual context. This study will present a frame of facial expression categories for effective manufacture of cartoon and animation that appeal to the visual emotion of the human.