• Journal of the Semiconductor & Display Technology
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• v.12 no.3
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• pp.53-57
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• 2013

Human emotions are expressed in various ways. It can be expressed through language, facial expression and gestures. In particular, the facial expression contains many information about human emotion. These vague human emotion appear not in single emotion, but in combination of various emotion. This paper proposes a emotional expression algorithm using Active Appearance Model(AAM) and Fuzz k- Nearest Neighbor which give facial expression in similar with vague human emotion. Applying Mahalanobis distance on the center class, determine inclusion level between center class and each class. Also following inclusion level, appear intensity of emotion. Our emotion recognition system can recognize a complex emotion using Fuzzy k-NN classifier.

• Journal of the Semiconductor & Display Technology
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• v.16 no.4
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• pp.79-85
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• 2017

Understanding of human emotion has a high importance in interaction between human and machine communications systems. The most expressive and valuable way to extract and recognize the human's emotion is by facial expression analysis. This paper presents and implements an automatic extraction and recognition scheme of facial expression and emotion through still image. This method has three main steps to recognize the facial emotion: (1) Detection of facial areas with skin-color method and feature maps, (2) Creation of the Bezier curve on eyemap and mouthmap, and (3) Classification and distinguish the emotion of characteristic with Hausdorff distance. To estimate the performance of the implemented system, we evaluate a success-ratio with emotional face image database, which is commonly used in the field of facial analysis. The experimental result shows average 76.1% of success to classify and distinguish the facial expression and emotion.

• Journal of the HCI Society of Korea
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• v.9 no.1
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• pp.23-28
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• 2014

In this study we focus on the effect of the interaction between humans and reactive television when emotion recognition through facial expression mechanism is used. Most of today's user interfaces in electronic products are passive and are not properly fitted into users' needs. In terms of the user centered device, we propose that the emotion based reactive television is the most effective in interaction compared to other passive input products. We have developed and researched next generation cognitive TV models in user centered. In this paper we present a result of the experiment that had been taken with Fraunhofer IIS $SHORE^{TM}$ demo software version to measure emotion recognition. This new approach was based on the real time cognitive TV models and through this approach we studied the relationship between humans and cognitive TV. This study follows following steps: 1) Cognitive TV systems can be on automatic ON/OFF mode responding to motions of people 2) Cognitive TV can directly select channels as face changes (ex, Neutral Mode and Happy Mode, Sad Mode, Angry Mode) 3) Cognitive TV can detect emotion recognition from facial expression of people within the fixed time and then if Happy mode is detected the programs of TV would be shifted into funny or interesting shows and if Angry mode is detected it would be changed to moving or touching shows. In addition, we focus on improving the emotion recognition through facial expression. Furthermore, the improvement of cognition TV based on personal characteristics is needed for the different personality of users in human to computer interaction. In this manner, the study on how people feel and how cognitive TV responds accordingly, plus the effects of media as cognitive mechanism will be thoroughly discussed.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1175-1186
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• 2017

Human emotion recognition is one of the promising applications in the era of artificial super intelligence. Thus far, facial expression traits are considered to be the most widely used information cues for realizing automated emotion recognition. This paper proposes a novel facial expression recognition (FER) method that works well for recognizing emotion from image sequences. To this end, we develop the so-called weighted soft voting classification (WSVC) algorithm. In the proposed WSVC, a number of classifiers are first constructed using different and multiple feature representations. In next, multiple classifiers are used for generating the recognition result (namely, soft voting) of each face image within a face sequence, yielding multiple soft voting outputs. Finally, these soft voting outputs are combined through using a weighted combination to decide the emotion class (e.g., anger) of a given face sequence. The weights for combination are effectively determined by measuring the quality of each face image, namely "peak expression intensity" and "frontal-pose degree". To test the proposed WSVC, CK+ FER database was used to perform extensive and comparative experimentations. The feasibility of our WSVC algorithm has been successfully demonstrated by comparing recently developed FER algorithms.

• The Journal of the Korea Contents Association
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• v.14 no.9
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• pp.476-483
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• 2014

The purpose of this study was to make a suitable images for korean emotional expressions. KSFI(Korean Standard Facial Image)-AUs was produced from korean standard apperance and FACS(Facial Action coding system)-AUs. For the objectivity of KSFI, the survey was examined about emotion recognition rate and contribution of emotion recognition in facial elements from six-basic emotional expression images(sadness, happiness, disgust, fear, anger and surprise). As a result of the experiment, the images of happiness, surprise, sadness and anger which had shown higher accuracy. Also, emotional recognition rate was mainly decided by the facial element of eyes and a mouth. Through the result of this study, KSFI contents which could be combined AU images was proposed. In this future, KSFI would be helpful contents to improve emotion recognition rate.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.105-110
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• 2008

Human being recognizes emotion fusing information of the other speech signal, expression, gesture and bio-signal. Computer needs technologies that being recognized as human do using combined information. In this paper, we recognized five emotions (normal, happiness, anger, surprise, sadness) through speech signal and facial image, and we propose to method that fusing into emotion for emotion recognition result is applying to multimodal method. Speech signal and facial image does emotion recognition using Principal Component Analysis (PCA) method. And multimodal is fusing into emotion result applying fuzzy membership function. With our experiments, our average emotion recognition rate was 63% by using speech signals, and was 53.4% by using facial images. That is, we know that speech signal offers a better emotion recognition rate than the facial image. We proposed decision fusion method using S-type membership function to heighten the emotion recognition rate. Result of emotion recognition through proposed method, average recognized rate is 70.4%. We could know that decision fusion method offers a better emotion recognition rate than the facial image or speech signal.

• Journal of the Korea Computer Graphics Society
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• v.24 no.4
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• pp.1-9
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• 2018

We propose an emotional training framework that can determine the initial symptom of schizophrenia by using emotional analysis method through facial expression change. We use Emotion API in Microsoft to obtain facial expressions and emotion values at the present time. We analyzed these values and recognized subtle facial expressions that change with time. The emotion states were classified according to the peak analysis-based variance method in order to measure the emotions appearing in facial expressions according to time. The proposed method analyzes the lack of emotional recognition and expressive ability by using characteristics that are different from the emotional state changes classified according to the six basic emotions proposed by Ekman. As a result, the analyzed values are integrated into the image color transfer framework so that users can easily recognize and train their own emotional changes.

• Science of Emotion and Sensibility
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• v.17 no.1
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• pp.53-62
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• 2014

Despite the huge potential of the practical application of emotion recognition technologies, the enhancement of the technologies still remains a challenge mainly due to the difficulty of recognizing emotion. Although not perfect, human emotions can be recognized through human images and sounds. Emotion recognition technologies have been researched by extensive studies that include image-based recognition studies, sound-based studies, and both image and sound-based studies. Studies on emotion recognition through facial expression detection are especially effective as emotions are primarily expressed in human face. However, differences in user environment and their familiarity with the technologies may cause significant disparities and errors. In order to enhance the accuracy of real-time emotion recognition, it is crucial to note a mechanism of understanding and analyzing users' personality traits that contribute to the improvement of emotion recognition. This study focuses on analyzing users' personality traits and its application in the emotion recognition system to reduce errors in emotion recognition through facial expression detection and improve the accuracy of the results. In particular, the study offers a practical solution to users with subtle facial expressions or low degree of emotion expression by providing an enhanced emotion recognition function.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.653-662
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• 2006

In ubiquitous computing that is to build computing environments to provide proper services according to user's context, human being's emotion recognition based on facial expression is used as essential means of HCI in order to make man-machine interaction more efficient and to do user's context-awareness. This paper addresses a problem of rigidly basic emotion recognition in context-sensitive facial expressions through a new Bayesian classifier. The task for emotion recognition of facial expressions consists of two steps, where the extraction step of facial feature is based on a color-histogram method and the classification step employs a new Bayesian teaming algorithm in performing efficient training and test. New context-sensitive Bayesian learning algorithm of EADF(Extended Assumed-Density Filtering) is proposed to recognize more exact emotions as it utilizes different classifier complexities for different contexts. Experimental results show an expression classification accuracy of over 91% on the test database and achieve the error rate of 10.6% by modeling facial expression as hidden context.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.1-6
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• 2005

In this paper, we propose an emotion recognition method using the facial images and speech signals. Six basic emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. Facia] expression recognition is performed by using the multi-resolution analysis based on the discrete wavelet. Here, we obtain the feature vectors through the ICA(Independent Component Analysis). On the other hand, the emotion recognition from the speech signal method has a structure of performing the recognition algorithm independently for each wavelet subband and the final recognition is obtained from the multi-decision making scheme. After merging the facial and speech emotion recognition results, we obtained better performance than previous ones.