• 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 Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.371-376
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• 2003

In this paper, we proposes a method for recognizing the facial expressions using ICA(Independent Component Analysis)-factorial representation method. Facial expression recognition consists of two stages. First, a method of Feature extraction transforms the high dimensional face space into a low dimensional feature space using PCA(Principal Component Analysis). And then, the feature vectors are extracted by using ICA-factorial representation method. The second recognition stage is performed by using the Euclidean distance measure based KNN(K-Nearest Neighbor) algorithm. We constructed the facial expression database for six basic expressions(happiness, sadness, angry, surprise, fear, dislike) and obtained a better performance than previous works.

• 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.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.3
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• pp.284-291
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• 2005

I propose a framework for detecting, recognizing and classifying facial features based on learned expression patterns. The framework recognizes facial expressions by using PCA and expression HMM(EHMM) which is Hidden Markov Model (HMM) approach to represent the spatial information and the temporal dynamics of the time varying visual expression patterns. Because the low level spatial feature extraction is fused with the temporal analysis, a unified spatio-temporal approach of HMM to common detection, tracking and classification problems is effective. The proposed recognition framework is accomplished by applying posterior probability between current visual observations and previous visual evidences. Consequently, the framework shows accurate and robust results of recognition on as well simple expressions as basic 6 facial feature patterns. The method allows us to perform a set of important tasks such as facial-expression recognition, HCI and key-frame extraction.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.806-813
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• 2010

Human facial expression shows human's emotion most exactly, so it can be used as the most efficient tool for delivering human's intention to computer. For fast and exact recognition of human's facial expression on a 2D image, this paper proposes a new method which integrates an Discrete Adaboost classification algorithm and a neural network based recognition algorithm. In the first step, Adaboost algorithm finds the position and size of a face in the input image. Second, input detected face image into 5 Adaboost strong classifiers which have been trained for each facial expressions. Finally, neural network based recognition algorithm which has been trained with the outputs of Adaboost strong classifiers determines final facial expression result. The proposed algorithm guarantees the realtime and enhanced accuracy by utilizing fastness and accuracy of Adaboost classification algorithm and reliability of neural network based recognition algorithm. In this paper, the proposed algorithm recognizes five facial expressions such as neutral, happiness, sadness, anger and surprise and achieves 86~95% of accuracy depending on the expression types in real time.

• 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.

• Journal of Internet Computing and Services
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• v.5 no.6
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• pp.79-91
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• 2004

Various facial pose detection and recognition has been a difficult problem. The problem is due to the fact that the distribution of various poses in a feature space is mere dispersed and more complicated than that of frontal faces, This thesis proposes a robust pose-expression-invariant face recognition method in order to overcome insufficiency of the existing face recognition system. First, we apply the TSL color model for detecting facial region and estimate the direction of face using facial features. The estimated pose vector is decomposed into X-V-Z axes, Second, the input face is mapped by deformable template using this vectors and 3D CANDIDE face model. Final. the mapped face is transformed to frontal face which appropriates for face recognition by the estimated pose vector. Through the experiments, we come to validate the application of face detection model and the method for estimating facial poses, Moreover, the tests show that recognition rate is greatly boosted through the normalization of the poses and expressions.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.11-15
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• 2002

Activities of venter frontalis, corrugator, levator labii superioris and greater zygomatic muscles were measured for five male subjects while they made pleasant, unpleasant and neutral facial expressions, and while they were presented pleasant, disgusting, and neutral odors. Pleasant expression and odor activated zygomatic muscles while unpleasant expression and odor increased corrugator muscle activity.

• Science of Emotion and Sensibility
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• v.11 no.4
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• pp.613-628
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• 2008

Mind reading is an essential part of normal social functioning and empathy plays a key role in social understanding. This study investigated how individual differences can have an effect on reading emotions in facial expressions, focusing on empathizing and systemizing. Two experiments were conducted. In study 1, participants performed emotion recognition test using facial expressions to investigate how emotion recognition can be different as empathy-systemizing type, facial areas, and emotion type. Study 2 examined how emotion recognition can be different as empathy-systemizing type, facial areas, and emotion type. An emotion discrimination test was used instead, with every other condition the same as in studies 1. Results from study 2 showed mostly same results as study 1: there were significant differences among facial areas and emotion type and also have an interaction effect between facial areas and emotion type. On the other hand, there was an interaction effect between empathy-systemizing type and emotion type in study 2. That is, how much people empathize and systemize can make difference in emotional discrimination. These results suggested that the empathy-systemizing type was more appropriate to explain emotion discrimination than emotion recognition.

• Smart Media Journal
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• v.13 no.3
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• pp.9-17
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• 2024

Due to the recent pandemic and the development of ICT technology, the use of non-face-to-face and unmanned systems is expanding, and it is very important to understand emotions in communication in non-face-to-face situations. As emotion recognition methods for various facial expressions are required to understand emotions, artificial intelligence-based research is being conducted to improve facial expression emotion recognition in image data. However, existing research on facial expression emotion recognition requires high computing power and a lot of learning time because it utilizes a large amount of data to improve accuracy. To improve these limitations, this paper proposes a method of recognizing facial expressions using age and gender, which are image meta information, as a method of recognizing facial expressions with even a small amount of data. For facial expression emotion recognition, a face was detected using the Yolo Face model from the original image data, and age and gender were classified through the VGG model based on image meta information, and then seven emotions were recognized using the EfficientNet model. The accuracy of the proposed data classification learning model was higher as a result of comparing the meta-information-based data classification model with the model trained with all data.