• Journal of Information Science Theory and Practice
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• v.7 no.2
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• pp.32-39
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• 2019

A convolutional neural network (CNN) has been widely used in facial expression recognition (FER) because it can automatically learn discriminative appearance features from an expression image. To make full use of its discriminating capability, this paper suggests a simple but effective method for CNN based FER. Specifically, instead of an original expression image that contains facial appearance only, the expression image with facial geometry visualization is used as input to CNN. In this way, geometric and appearance features could be simultaneously learned, making CNN more discriminative for FER. A simple CNN extension is also presented in this paper, aiming to utilize geometric expression change derived from an expression image sequence. Experimental results on two public datasets (CK+ and MMI) show that CNN using facial geometry visualization clearly outperforms the conventional CNN using facial appearance only.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.2
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• pp.328-333
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• 2003

This paper did to do with 18 muscle pairs that do fetters in anatomy that influence in facial expression change and mix motion of muscle for face facial animation. After set and change mash and make standard model in individual's image, did mapping to mash using individual facial front side and side image to raise truth stuff. Muscle model who become motive power that can do animation used facial expression creation correcting Waters' muscle model. Created deformed face that texture is dressed using these method. Also, 6 facial expression that Ekman proposes did animation.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.371-376
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• 2007

This paper presents an emotion recognition and its expression system of an intelligent robot like a home robot or a service robot. Emotion recognition method in the robot is used by a facial image. We use a motion and a position of many facial features. apply a tracking algorithm to recognize a moving user in the mobile robot and eliminate a skin color of a hand and a background without a facial region by using the facial region detecting algorithm in objecting user image. After normalizer operations are the image enlarge or reduction by distance of the detecting facial region and the image revolution transformation by an angel of a face, the mobile robot can object the facial image of a fixing size. And materialize a multi feature selection algorithm to enable robot to recognize an emotion of user. In this paper, used a multi layer perceptron of Artificial Neural Network(ANN) as a pattern recognition art, and a Back Propagation(BP) algorithm as a learning algorithm. Emotion of user that robot recognized is expressed as a graphic LCD. At this time, change two coordinates as the number of times of emotion expressed in ANN, and change a parameter of facial elements(eyes, eyebrows, mouth) as the change of two coordinates. By materializing the system, expressed the complex emotion of human as the avatar of LCD.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.3 s.309
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• pp.67-75
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• 2006

Facial expressions exhibit not only facial feature motions, but also subtle changes in illumination and appearance. Since it is difficult to generate realistic facial expressions by using only geometric deformations, detailed features such as textures should also be deformed to achieve more realistic expression. The existing methods such as the expression ratio image have drawbacks, in that detailed changes of complexion by lighting can not be generated properly. In this paper, we propose a nonlinear model for skin color change and a model-based synthesis method for facial expression that can apply realistic expression details under different lighting conditions. The proposed method is composed of the following three steps; automatic extraction of facial features using active appearance model and geometric deformation of expression using warping, generation of facial expression using a model for nonlinear skin color change, and synthesis of original face with generated expression using a blending ratio that is computed by the Euclidean distance transform. Experimental results show that the proposed method generate realistic facial expressions under various lighting conditions.

• Korean Journal of Culture and Social Issue
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• v.20 no.4
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• pp.347-364
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• 2014

The present study was conducted to investigate how emotional expression change, test delay, and background influence on face recognition. In experiment 1, participants were presented with negative faces at study phase and administered for standard old-new recognition test including targets of negative and neutral expression for the same faces. In experiment 2, participants were studied negative faces and tested by old-new face recognition test with targets of negative and positive faces. In experiment 3, participants were presented with neutral faces at study phase and had to identify the same faces with no regard for negative and neutral expression at face recognition test. In all three experiments, participants were assigned into either immediate test or delay test, and target faces were presented in both white and black background. Results of experiments 1 and 2 indicated higher rates for negative faces than neutral or positive faces. Facial expression consistency enhanced face recognition memory. In experiment 3, the superiority of facial expression consistency were demonstrated by higher rates for neutral faces at recognition test. If facial expressions were consistent across encoding and retrieval, memory performance on face recognition were enhanced in all three experiments. And the effect of facial expression change have different effects on background conditions. The findings suggest that facial expression change make face identification hard, and time and background also affect on face recognition.

• Convergence Security Journal
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• v.19 no.1
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• pp.67-78
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• 2019

Most existing facial expression recognition methods use a uniform grid method that divides the entire facial image into uniform blocks when describing facial features. The problem of this method may include non-face backgrounds, which interferes with discrimination of facial expressions, and the feature of a face included in each block may vary depending on the position, size, and orientation of the face in the input image. In this paper, we propose a variable-size block method which determines the size and position of a block that best represents meaningful facial expression change. As a part of the effort, we propose the way to determine the optimal number, position and size of each block based on the facial feature points. For the evaluation of the proposed method, we generate the facial feature vectors using LDTP and construct a facial expression recognition system based on SVM. Experimental results show that the proposed method is superior to conventional uniform grid based method. Especially, it shows that the proposed method can adapt to the change of the input environment more effectively by showing relatively better performance than exiting methods in the images with large shape and orientation changes.

• 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.

• Journal of Physiology & Pathology in Korean Medicine
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• v.26 no.2
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• pp.253-258
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• 2012

Facial expression was an important communication methods. In oriental medicine, according to the emotion the face has changed shape and difference occurs in physiology and pathology. To verify such a theory, we studied the correlation between emotional facial expressions and meridian and collateral flow. The facial region divided by meridian, outer brow was Gallbladder meridian, inner brow was Bladder meridian, medial canthus was Bladder meridian, lateral canthus was Gallbladder meridian, upper eyelid was Bladder meridian, lower eyelid was Stomach meridian, central cheeks was Stomach meridian, lateral cheeks was Small intestine meridian, upper and lower lips, lip corner, chin were Small and Large intestine meridian. Meridian and collateral associated with happiness was six. This proves happiness is a high importance on facial expression. Meridian and collateral associated with anger was five. Meridian and Collateral associated with fear and sadness was four. This shows fear and sadness are a low importance on facial expression than different emotion. Based on yang meridian which originally descending flow in the body, the ratio of anterograde and retrograde were happiness 3:4, angry 2:5, sadness 5:3, fear 4:1. Based on face of the meridian flow, the ratio of anterograde and retrograde were happiness 5:2, angry 3:4, sadness 3:5, fear 4:1. We found out that practical meridian and collateral flow change by emotion does not correspond to the expected meridian and collateral flow change by emotion.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.9
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• pp.2349-2357
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• 1994

This paper presents a facial expression transformation algorithm and drawing rule generation algolithm for a portrait drawing robot which was developed for the '93 Taejeon EXPO. The developed algorithm was mainly focused on the robust automatic generation of robot programs with the consideration that the drawing robot should work without any limitation of the age, sex or race for the persons. In order to give more demonstratin effects, the facial expression change of the pictured person was performed.

• Journal of the Semiconductor & Display Technology
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• v.18 no.1
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• pp.97-101
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• 2019

Understanding and classification of the human's emotion play an important tasks in interacting with human and machine communication systems. This paper proposes a novel emotion recognition method by extracting facial keypoints, which is able to understand and classify the human emotion, using active Appearance Model and the proposed classification model of the facial features. The existing appearance model scheme takes an expression of variations, which is calculated by the proposed classification model according to the change of human facial expression. The proposed method classifies four basic emotions (normal, happy, sad and angry). To evaluate the performance of the proposed method, we assess the ratio of success with common datasets, and we achieve the best 93% accuracy, average 82.2% in facial emotion recognition. The results show that the proposed method effectively performed well over the emotion recognition, compared to the existing schemes.