• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.333-340
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• 2022

The COVID-19 has made everyone anxious and people need to keep their distance. It is necessary to conduct collective assessment and screening of college students' mental health in the opening season of every year. This study uses and trains a multi-layer perceptron neural network model for deep learning to identify facial emotions. After the training, real pictures and videos were input for face detection. After detecting the positions of faces in the samples, emotions were classified, and the predicted emotional results of the samples were sent back and displayed on the pictures. The results show that the accuracy is 93.2% in the test set and 95.57% in practice. The recognition rate of Anger is 95%, Disgust is 97%, Happiness is 96%, Fear is 96%, Sadness is 97%, Surprise is 95%, Neutral is 93%, such efficient emotion recognition can provide objective data support for capturing negative. Deep learning emotion recognition system can cooperate with traditional psychological activities to provide more dimensions of psychological indicators for health.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.8
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• pp.235-242
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• 2023

In recent years, as online counseling for infants and adolescents has increased, CNN-based deep learning models are widely used as assistance tools for emotion recognition. However, since most emotion recognition models are trained on mainly adult data, there are performance restrictions to apply the model to infants and adolescents. In this paper, in order to analyze the performance constraints, the characteristics of facial expressions for emotional recognition of infants and adolescents compared to adults are analyzed through LIME method, one of the XAI techniques. In addition, the experiments are performed on the male and female groups to analyze the characteristics of gender-specific facial expressions. As a result, we describe age-specific and gender-specific experimental results based on the data distribution of the pre-training dataset of CNN models and highlight the importance of balanced learning data.

• Journal of Korea Multimedia Society
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• v.10 no.10
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• pp.1310-1318
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• 2007

First of all, What was the modeling technique used to model 3D-Game character? It's a technique developed along several years, by experience... here is the bases Low polygons characters I always work in low polygon for two reasons -You can easily modify a low-poly character, change shapes, make morph for facial expressions etc -You can easily animate a low-poly character When the modeling is finished, Second, In these days, Computer hardware technologies have been bring about that expansion of various 3D digital motion pictured information and development. 3D digital techniques can be used to be diversity in Animation, Virtual-Reality, Movie, Advertisement, Game and so on. Besides, as computing power has been better and higher, the development of 3D Animations and Character are required gradually. In order to satisfy the requirement, Research about how to make 3D Game modeling that represents Character's emotions, sensibilities, is beginning to set its appearance. 3D characters in 3D Games are the core for the communications of emotion and the informations through their facial expression and characteristic motions, Sounds to Users. All concerning about 3D motion and facial expression are getting higher with extension of frequency in use. Therefore, in this study we suggest the effective method of modeling for 3D character and which are based on 2D Graphics.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.123-128
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• 2016

The study was conducted to confirm the analgesic effects of the Carbenoxolone(CBX)and P2X receptor antagonist(iso-PPADS), which separates the gap junction in the facial neuropathic pain model. The experiment used white male Sprague-Dawley rats (240~280g). The second left molars on the lower jaw was extracted to induce facial neuropathic pain, and small dental implants were implanted to induce damage to the inferior alveolar nerve. When CBX was injected twice daily to the abdominal cavity, a significant analgesic effect at 5ug/kg was observed(p<0.05). In addition, when iso-PPADS was injected twice daily into the abdominal cavity, a significant analgesic reaction was observed at $25{\mu}g/kg$(p<0.05). When the two drugs were injected together at a low concentration, in which they did not display an effect, they displayed a significant analgesic reaction at CBX 1ug/kg and iso-PPADS 2.5ug/kg(p<0.05). When a gap injunction block using a low concentration of CBX and a low concentration P2X receptor antagonist was injected together, the pain suppressing effect was observed against the orofacial neuropathic pain mechanism. These results make it possible to determine that the gap junction block using CBX and the injection of the P2X receptor antagonist plays an important role in the pain management of the facial region.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.1
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• pp.59-73
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• 2003

As a means of overcoming the linguistic barrier between different languages in the Internet, a new sign-language communication system with CG animation techniques has been developed and proposed. In the system, the joint angles of the arms and the hands corresponding to the gesture as a non-verbal communication tool have been considered. The emotional expression, however, could as play also an important role in communicating each other. Especially, a comic expression is more efficient than real facial expression, and the movements of the cheeks and the jaws are more important AU's than those of the eyebrow, eye, mouth etc. Therefore, in this paper, we designed a 3D emotion editor using 2D model, and we extract AU's (called as PAU, here) which play a principal function in expressing emotions. We also proposed a method of generating the universal emotional expression with Avatar models which have different vertex structures. Here, we employed a method of dynamically adjusting the AU movements according to emotional intensities. The proposed system is implemented with Visual C＋＋ and Open Inventor on windows platforms. Experimental results show a possibility that the system could be used as a non-verbal communication means to overcome the linguistic barrier.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.794-807
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• 2022

Although registration of dogs is mandatory according to the revision of the Animal Protection Act, the registration rate is low due to the inconvenience of the current registration method. In this paper, a performance improvement study was conducted on the dog face recognition technology, which is being reviewed as a new registration method. Through deep learning learning, an embedding vector for facial recognition of a dog was created and a method for identifying each dog individual was experimented. We built a dog image dataset for deep learning learning and experimented with InceptionNet and ResNet-50 as backbone networks. It was learned by the triplet loss method, and the experiments were divided into face verification and face recognition. In the ResNet-50-based model, it was possible to obtain the best facial verification performance of 93.46%, and in the face recognition test, the highest performance of 91.44% was obtained in rank-5, respectively. The experimental methods and results presented in this paper can be used in various fields, such as checking whether a dog is registered or not, and checking an object at a dog access facility.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.299-308
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• 2014

Various literatures related computing of information processing have been recently shown the researches inspired from the remarkably excellent human capabilities which recognize and categorize very complex visual patterns such as body motions and facial expressions. Applied from human's outstanding ability of perception, the classification function of visual sequences without context information is specially crucial task for computer vision to understand both the coding and the retrieval of spatio-temporal patterns. This paper presents a biological process based action recognition model of computer vision, which is inspired from visual information processing of human brain for action recognition of visual sequences. Proposed model employs the structure of neural fields of bio-inspired visual perception on detecting motion sequences and discriminating visual patterns in human brain. Experimental results show that proposed recognition model takes not only into account several biological properties of visual information processing, but also is tolerant of time-warping. Furthermore, the model allows robust temporal evolution of classification compared to researches of action recognition. Presented model contributes to implement bio-inspired visual processing system such as intelligent robot agent, etc.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1998.11a
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• pp.181-185
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• 1998

Recently, research and developmental direction of communication system is concurrent adopting voice data and face image in speaking to provide more higher recognition rate then in the case of only voice data. Therefore, we present a method of lipreading in speech image sequence by using the 3-D facial shape model. The method use a feature information of the face image such as the opening-level of lip, the movement of jaw, and the projection height of lip. At first, we adjust the 3-D face model to speeching face image sequence. Then, to get a feature information we compute variance quantity from adjusted 3-D shape model of image sequence and use the variance quality of the adjusted 3-D model as recognition parameters. We use the intensity inclination values which obtaining from the variance in 3-D feature points as the separation of recognition units from the sequential image. After then, we use discrete HMM algorithm at recognition process, depending on multiple observation sequence which considers the variance of 3-D feature point fully. As a result of recognition experiment with the 8 Korean vowels and 2 Korean consonants, we have about 80% of recognition rate for the plosives and vowels.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.5
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• pp.783-788
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• 2002

Recently, research and developmental direction of communication system is concurrent adopting voice data and face image in speaking to provide more higher recognition rate then in the case of only voice data. Therefore, we present a method of lipreading in speech image sequence by using the 3-D facial shape model. The method use a feature information of the face image such as the opening-level of lip, the movement of jaw, and the projection height of lip. At first, we adjust the 3-D face model to speeching face Image sequence. Then, to get a feature information we compute variance quantity from adjusted 3-D shape model of image sequence and use the variance quality of the adjusted 3-D model as recognition parameters. We use the intensity inclination values which obtaining from the variance in 3-D feature points as the separation of recognition units from the sequential image. After then, we use discrete HMM algorithm at recognition process, depending on multiple observation sequence which considers the variance of 3-D feature point fully. As a result of recognition experiment with the 8 Korean vowels and 2 Korean consonants, we have about 80% of recognition rate for the plosives md vowels.