• Journal of KIISE:Software and Applications
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• v.27 no.8
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• pp.827-833
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• 2000

Facial expression is an effective tool to express human emotion. In this paper, a facial expression analysis method based on the base faces and their blending ratio is proposed. The seven base faces were chosen as axes describing and analyzing arbitrary facial expression. We set up seven facial expressions such as, surprise, fear, anger, disgust, happiness, sadness, and expressionless as base faces. Facial expression was built by fitting generic 3D facial model to facial image. Two comparable methods, Genetic Algorithms and Simulated Annealing were used to search the blending ratio of base faces. The usefulness of the proposed method for facial expression analysis was proved by the facial expression synthesis results.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.255-263
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• 2006

In the last decade, face analysis, e.g. face detection, face recognition, facial expression recognition, is a very lively and expanding research field. As computer animated agents and robots bring a social dimension to human computer interaction, interest in this research field is increasing rapidly. In this paper, we introduce an artificial emotion mimic system which can recognize human facial expressions and also generate the recognized facial expression. In order to recognize human facial expression in real-time, we propose a facial expression classification method that is performed by weak classifiers obtained by using new rectangular feature types. In addition, we make the artificial facial expression using the developed robotic system based on biological observation. Finally, experimental results of facial expression recognition and generation are shown for the validity of our robotic system.

• 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 Korea Computer Industry Society
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• v.8 no.3
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• pp.187-196
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• 2007

Human being represents his emotion through facial expression on purpose or unconsciously. Several psychologists started the research for analysis of facial expression, and over the last decade, many computer scientists were also interested in it. Facial expression recognition is a future-valuable research that can be applicable in many kinds of field based on man-computer interface. However, in spite of lots of study, it is hard to find any practical systems because of a variety of illumination and scale of face, and high dimensional information to be processed. In this paper, I tried to describe a generic framework for facial expression analysis, the need of each level, and international research tendency. Also, I analyzed the case study of facial expression in Korea. I expect it to be helpful for the scientists willing to make contribution on facial expression.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.323-333
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• 2023

Facial expression recognition can aid in the development of fatigue driving detection, teaching quality evaluation, and other fields. In this study, a facial expression recognition method was proposed with a residual masking reconstruction network as its backbone to achieve more efficient expression recognition and classification. The residual layer was used to acquire and capture the information features of the input image, and the masking layer was used for the weight coefficients corresponding to different information features to achieve accurate and effective image analysis for images of different sizes. To further improve the performance of expression analysis, the loss function of the model is optimized from two aspects, feature dimension and data dimension, to enhance the accurate mapping relationship between facial features and emotional labels. The simulation results show that the ROC of the proposed method was maintained above 0.9995, which can accurately distinguish different expressions. The precision was 75.98%, indicating excellent performance of the facial expression recognition model.

• 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 Ergonomics Society of Korea
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• v.31 no.3
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• pp.427-435
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• 2012

The aim of this study is to investigate facial temperature changes induced by facial expression and emotional state in order to recognize a persons emotion using facial thermal images. Background: Facial thermal images have two advantages compared to visual images. Firstly, facial temperature measured by thermal camera does not depend on skin color, darkness, and lighting condition. Secondly, facial thermal images are changed not only by facial expression but also emotional state. To our knowledge, there is no study to concurrently investigate these two sources of facial temperature changes. Method: 231 students participated in the experiment. Four kinds of stimuli inducing anger, fear, boredom, and neutral were presented to participants and the facial temperatures were measured by an infrared camera. Each stimulus consisted of baseline and emotion period. Baseline period lasted during 1min and emotion period 1~3min. In the data analysis, the temperature differences between the baseline and emotion state were analyzed. Eyes, mouth, and glabella were selected for facial expression features, and forehead, nose, cheeks were selected for emotional state features. Results: The temperatures of eyes, mouth, glanella, forehead, and nose area were significantly decreased during the emotional experience and the changes were significantly different by the kind of emotion. The result of linear discriminant analysis for emotion recognition showed that the correct classification percentage in four emotions was 62.7% when using both facial expression features and emotional state features. The accuracy was slightly but significantly decreased at 56.7% when using only facial expression features, and the accuracy was 40.2% when using only emotional state features. Conclusion: Facial expression features are essential in emotion recognition, but emotion state features are also important to classify the emotion. Application: The results of this study can be applied to human-computer interaction system in the work places or the automobiles.

• 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 Electrical Engineering and Technology
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• v.12 no.4
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• pp.1657-1662
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• 2017

Facial expression recognition systems using video devices have emerged as an important component of natural human-machine interfaces which contribute to various practical applications such as security systems, behavioral science and clinical practices. In this work, we present a new method to analyze, represent and recognize human facial expressions using a sequence of facial images. Under our proposed facial expression recognition framework, the overall procedure includes: accurate face detection to remove background and noise effects from the raw image sequences and align each image using vertex mask generation. Furthermore, these features are reduced by principal component analysis. Finally, these augmented features are trained and tested using Hidden Markov Model (HMM). The experimental evaluation demonstrated the proposed approach over two public datasets such as Cohn-Kanade and AT&T datasets of facial expression videos that achieved expression recognition results as 96.75% and 96.92%. Besides, the recognition results show the superiority of the proposed approach over the state of the art methods.

• 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.