• Convergence Security Journal
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• v.12 no.4
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• pp.41-47
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• 2012

For competitiveness of national B2C (Business to Customer) service industry, improvement of process and analysis focused on customer and change of service system are needed. In other words, a business and an organization should deduce and provide what kind of services customers want. Then, evaluate customers' satisfaction and improve the service quality. To achieve this goal, accurate feedbacks from customers play an important role; however, there are not quantitative and standard systems a lot in nation. Recently, the researches about ICT (Information and Communication Technology) that can recognize emotion of human being are on the increase. The facial expression recognition among them is known as most efficient and natural human interface. This research analyzes about more efficient facial expression recognition and suggests a customer satisfaction feedback system using that.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1760-1763
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• 2002

In this paper the authors present their attempt io realize a motion pattern detector that finds specified sequence of image from input motion image. The detector is intended to be used for time-varying facial expression understanding. Needless to say, facial expression understanding by machine is crucial and enriches quality of human machine interaction. Among various facial expressions, like blinking, there must be such expressions that can not be recognized if input expression image is static. Still image of blinking can not be distinguished from sleeping. In this paper, the authors discuss implementation of their motion pattern detector and describe experiments using the detector. Experimental results confirm the feasibility of the idea behind the implemented detector.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.86-92
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• 2015

This study proposes new LDP code to improve facial expression recognition rate by including local directional number(LDN), edge magnitudes and differences of neighborhood edge intensity. LDP is less sensitive on the change of intensity and stronger about noise than LBP. But LDP is difficult to express the smooth area without changing of intensity and if background image has the similar pattern with a face, the facial expression recognition rate of LDP is low. Therefore, we make the LDP code has the local directional number and the edge strength and experiment the facial expression recognition rate of changed LDP code.

• 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.

• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.375-385
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• 2024

In the context of contemporary digital media, virtual influencers have become an increasingly important form of socialization and entertainment, in which emotional expression is a key factor in attracting viewers. In this study, we take Luo Tianyi, a Chinese virtual influencer, as an example to explore how emotions are expressed and perceived through facial expressions in different types of videos. Using Paul Ekman's Facial Action Coding System (FACS) and six basic emotion classifications, the study systematically analyzes Luo Tianyi's emotional expressions in three types of videos, namely Music show, Festivals and Brand Cooperation. During the study, Luo Tianyi's facial expressions and emotional expressions were analyzed through rigorous coding and categorization, as well as matching the context of the video content. The results show that Enjoyment is the most frequently expressed emotion by Luo Tianyi, reflecting the centrality of positive emotions in content creation. Meanwhile, the presence of other emotion types reveals the virtual influencer's efforts to create emotionally rich and authentic experiences. The frequency and variety of emotions expressed in different video genres indicate Luo Tianyi's diverse strategies for communicating and connecting with viewers in different contexts. The study provides an empirical basis for understanding and utilizing virtual influencers' emotional expressions, and offers valuable insights for digital media content creators to design emotional expression strategies. Overall, this study is valuable for understanding the complexity of virtual influencer emotional expression and its importance in digital media strategy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.1
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• pp.117-122
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• 2005

This paper proposes an algorithm for Facial Characteristic Point(FCP) extraction. The FCP plays an important role in expression representation for face animation, avatar mimic or facial expression recognition. Conventional algorithms extract the FCP with an expensive motion capture device or by using markers, which give an inconvenience or a psychological load to experimental person. However, the proposed algorithm solves the problems by using only image processing. For the efficient FCP extraction, we analyze and improve the conventional algorithms detecting facial components, which are basis of the FCP extraction.

• 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.

• Journal of Biomedical Engineering Research
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• v.20 no.4
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• pp.419-425
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• 1999

In this paper, we present an algorithm of real time facial expression and gesture recognition for image sequence on the gray level. A mixture algorithm of a template matching and knowledge based geometrical consideration of a face were adapted to locate the face area in input image. And optical flow method applied on the area to recognize facial expressions. Also, we suggest hand area detection algorithm form a background image by analyzing entropy in an image. With modified hand area detection algorithm, it was possible to recognize hand gestures from it. As a results, the experiments showed that the suggested algorithm was good at recognizing one's facial expression and hand gesture by detecting a dominant motion area on images without getting any limits from the background image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.6000-6017
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• 2018

This paper presents a new descriptor, named Histograms of Prominent Edge Directions (HPED), for the recognition of facial expressions in a person-independent environment. In this paper, we raise the issue of sampling error in generating the code-histogram from spatial regions of the face image, as observed in the existing descriptors. HPED describes facial appearance changes based on the statistical distribution of the top two prominent edge directions (i.e., primary and secondary direction) captured over small spatial regions of the face. Compared to existing descriptors, HPED uses a smaller number of code-bins to describe the spatial regions, which helps avoid sampling error despite having fewer samples while preserving the valuable spatial information. In contrast to the existing Histogram of Oriented Gradients (HOG) that uses the histogram of the primary edge direction (i.e., gradient orientation) only, we additionally consider the histogram of the secondary edge direction, which provides more meaningful shape information related to the local texture. Experiments on popular facial expression datasets demonstrate the superior performance of the proposed HPED against existing descriptors in a person-independent environment.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.193-201
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• 2014

This paper suggests a facial expression recognition system using face detection, face alignment, facial unit extraction, and training and testing algorithms based on AdaBoost classifiers. First, we find face region by a face detector. From the results, face alignment algorithm extracts feature points. The facial units are from a subset of action units generated by combining the obtained feature points. The facial units are generally more effective for smaller-sized databases, and are able to represent the facial expressions more efficiently and reduce the computation time, and hence can be applied to real-time scenarios. Experimental results in real scenarios showed that the proposed system has an excellent performance over 90% recognition rates.