• Title, Summary, Keyword: recognition

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Analogical Face Generation based on Feature Points

  • Yoon, Andy Kyung-yong;Park, Ki-cheul;Oh, Duck-kyo;Cho, Hye-young;Jang, Jung-hyuk
    • Journal of Multimedia Information System
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    • v.6 no.1
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    • pp.15-22
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    • 2019
  • There are many ways to perform face recognition. The first step of face recognition is the face detection step. If the face is not found in the first step, the face recognition fails. Face detection research has many difficulties because it can be varied according to face size change, left and right rotation and up and down rotation, side face and front face, facial expression, and light condition. In this study, facial features are extracted and the extracted features are geometrically reconstructed in order to improve face recognition rate in extracted face region. Also, it is aimed to adjust face angle using reconstructed facial feature vector, and to improve recognition rate for each face angle. In the recognition attempt using the result after the geometric reconstruction, both the up and down and the left and right facial angles have improved recognition performance.

Multi-resolution DenseNet based acoustic models for reverberant speech recognition (잔향 환경 음성인식을 위한 다중 해상도 DenseNet 기반 음향 모델)

  • Park, Sunchan;Jeong, Yongwon;Kim, Hyung Soon
    • Phonetics and Speech Sciences
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    • v.10 no.1
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    • pp.33-38
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    • 2018
  • Although deep neural network-based acoustic models have greatly improved the performance of automatic speech recognition (ASR), reverberation still degrades the performance of distant speech recognition in indoor environments. In this paper, we adopt the DenseNet, which has shown great performance results in image classification tasks, to improve the performance of reverberant speech recognition. The DenseNet enables the deep convolutional neural network (CNN) to be effectively trained by concatenating feature maps in each convolutional layer. In addition, we extend the concept of multi-resolution CNN to multi-resolution DenseNet for robust speech recognition in reverberant environments. We evaluate the performance of reverberant speech recognition on the single-channel ASR task in reverberant voice enhancement and recognition benchmark (REVERB) challenge 2014. According to the experimental results, the DenseNet-based acoustic models show better performance than do the conventional CNN-based ones, and the multi-resolution DenseNet provides additional performance improvement.

Multimodal Parametric Fusion for Emotion Recognition

  • Kim, Jonghwa
    • International journal of advanced smart convergence
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    • v.9 no.1
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    • pp.193-201
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    • 2020
  • The main objective of this study is to investigate the impact of additional modalities on the performance of emotion recognition using speech, facial expression and physiological measurements. In order to compare different approaches, we designed a feature-based recognition system as a benchmark which carries out linear supervised classification followed by the leave-one-out cross-validation. For the classification of four emotions, it turned out that bimodal fusion in our experiment improves recognition accuracy of unimodal approach, while the performance of trimodal fusion varies strongly depending on the individual. Furthermore, we experienced extremely high disparity between single class recognition rates, while we could not observe a best performing single modality in our experiment. Based on these observations, we developed a novel fusion method, called parametric decision fusion (PDF), which lies in building emotion-specific classifiers and exploits advantage of a parametrized decision process. By using the PDF scheme we achieved 16% improvement in accuracy of subject-dependent recognition and 10% for subject-independent recognition compared to the best unimodal results.

FIGURE ALPHABET HYPOTHESIS INSPIRED NEURAL NETWORK RECOGNITION MODEL

  • Ohira, Ryoji;Saiki, Kenji;Nagao, Tomoharu
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • pp.547-550
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    • 2009
  • The object recognition mechanism of human being is not well understood yet. On research of animal experiment using an ape, however, neurons that respond to simple shape (e.g. circle, triangle, square and so on) were found. And Hypothesis has been set up as human being may recognize object as combination of such simple shapes. That mechanism is called Figure Alphabet Hypothesis, and those simple shapes are called Figure Alphabet. As one way to research object recognition algorithm, we focused attention to this Figure Alphabet Hypothesis. Getting idea from it, we proposed the feature extraction algorithm for object recognition. In this paper, we described recognition of binarized images of multifont alphabet characters by the recognition model which combined three-layered neural network in the feature extraction algorithm. First of all, we calculated the difference between the learning image data set and the template by the feature extraction algorithm. The computed finite difference is a feature quantity of the feature extraction algorithm. We had it input the feature quantity to the neural network model and learn by backpropagation (BP method). We had the recognition model recognize the unknown image data set and found the correct answer rate. To estimate the performance of the contriving recognition model, we had the unknown image data set recognized by a conventional neural network. As a result, the contriving recognition model showed a higher correct answer rate than a conventional neural network model. Therefore the validity of the contriving recognition model could be proved. We'll plan the research a recognition of natural image by the contriving recognition model in the future.

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Simple Frame Marker: Implementation of In-Marker Image and Character Recognition and Tracking Method (심플 프레임 마커: 마커 내부 이미지 및 문자 패턴의 인식 및 추적 기법 구현)

  • Kim, Hye-Jin;Woo, Woon-Tack
    • 한국HCI학회:학술대회논문집
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    • pp.558-561
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    • 2009
  • In this paper, we propose Simple Frame Marker(SFMarker) to support recognition of characters and images included in a marker in augmented reality. If characters are inserted inside of marker and are recognised using Optical Character Recognition(OCR), it doesn't need marker learning process before an execution. It also reduces visual disturbance compared to 2D barcode marker due to familarity of characters. Therefore, proposed SFMarker distinguishes Square SFMarker that embeds images from Rectangle SFMarker with characters according to ratio of marker and applies different recognition algorithms. Also, in order to reduce preprocessing of character recognition, SFMarker inserts direction information in border of marker and extracts it to execute character recognition fast and correctly. Finally, since the character recognition for every frame slows down tracking speed, we increase the speed of recognition process using the result of character recognition in previous frame when frame difference is low.

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The Influence of the Type of Single Females' Life Style in Their 20s through 30s on the Recognition of the Behavior for Beauty (20-30대 미혼여성의 라이프스타일 유형이 뷰티행동인식에 미치는 영향)

  • Hong, Soo-Nam
    • Journal of the Korea Fashion and Costume Design Association
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    • v.16 no.1
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    • pp.77-89
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    • 2014
  • This study looked into the effect of the life style of single females in 20s and 30s on beauty behavior recognition, and spss 17.0 is used for data analysis method. As for the statistical analysis method in order to validate the measurement tools, reliability verification is conducted and life style groups are sampled using K-means taking into account factor scores by life style. To find out the difference between general beauty behavior recognition and life style, descriptive statistics and One Way ANOVA were carried out, and Duncan Test was implemented for the post examination method. Multiple regression analysis was also carried out to figure out the effect of life style on beauty behavior recognition. The result is as follows. First, according to the results of reliability verification and factor analysis for the lifestyle type and the recognition of the behavior for beauty, the types of the life style of the subjects were divided into Economic Utility, Convention Conservatism, Self Development, Showy Consumption, and Appearance Oriented, and the recognition of the behavior for beauty was named as Makeup and Hair, Cosmetic Surgery, Body Care, and Skin Care. Second, as to the recognition of the behavior for beauty based upon the lifestyle, the Appearance Oriented in Showy Consumption recorded the highest. Third, the analysis of the influence of the style on the recognition of the behavior for beauty showed that the behavior recognition for Makeup and Hair and for Skin Care was affected by the life style of Self Development, Showy Consumption, and Appearance Oriented; the behavior recognition for Cosmetic Surgery was affected by the life style of Conventional Conservatism, Showy Consumption, and Appearance Oriented; and again the behavior recognition for Body Care was by that of Economical Utility and Showy Consumption.

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A Study on the Evaluation of Optimal Program Applicability for Face Recognition Using Machine Learning (기계학습을 이용한 얼굴 인식을 위한 최적 프로그램 적용성 평가에 대한 연구)

  • Kim, Min-Ho;Jo, Ki-Yong;You, Hee-Won;Lee, Jung-Yeal;Baek, Un-Bae
    • Korea Journal of Artificial Intelligence
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    • v.5 no.1
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    • pp.10-17
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    • 2017
  • This study is the first attempt to raise face recognition ability through machine learning algorithm and apply to CRM's information gathering, analysis and application. In other words, through face recognition of VIP customer in distribution field, we can proceed more prompt and subdivided customized services. The interest in machine learning, which is used to implement artificial intelligence, has increased, and it has become an age to automate it by using machine learning beyond the way that a person directly models an object recognition process. Among them, Deep Learning is evaluated as an advanced technology that shows amazing performance in various fields, and is applied to various fields of image recognition. Face recognition, which is widely used in real life, has been developed to recognize criminals' faces and catch criminals. In this study, two image analysis models, TF-SLIM and Inception-V3, which are likely to be used for criminal face recognition, were selected, analyzed, and implemented. As an evaluation criterion, the image recognition model was evaluated based on the accuracy of the face recognition program which is already being commercialized. In this experiment, it was evaluated that the recognition accuracy was good when the accuracy of the image classification was more than 90%. A limit of our study which is a way to raise face recognition is left as a further research subjects.

Sliding Active Camera-based Face Pose Compensation for Enhanced Face Recognition (얼굴 인식률 개선을 위한 선형이동 능동카메라 시스템기반 얼굴포즈 보정 기술)

  • 장승호;김영욱;박창우;박장한;남궁재찬;백준기
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.6
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    • pp.155-164
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    • 2004
  • Recently, we have remarkable developments in intelligent robot systems. The remarkable features of intelligent robot are that it can track user and is able to doface recognition, which is vital for many surveillance-based systems. The advantage of face recognition compared with other biometrics recognition is that coerciveness and contact that usually exist when we acquire characteristics do not exist in face recognition. However, the accuracy of face recognition is lower than other biometric recognition due to the decreasing in dimension from image acquisition step and various changes associated with face pose and background. There are many factors that deteriorate performance of face recognition such as thedistance from camera to the face, changes in lighting, pose change, and change of facial expression. In this paper, we implement a new sliding active camera system to prevent various pose variation that influence face recognition performance andacquired frontal face images using PCA and HMM method to improve the face recognition. This proposed face recognition algorithm can be used for intelligent surveillance system and mobile robot system.

A Study on Implications of Recognition Paradigm for Social Work (대안적 비판이론으로서 인정 패러다임의 사회복지적 함의)

  • Kim, Giduk
    • Korean Journal of Social Welfare
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    • v.67 no.4
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    • pp.325-348
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    • 2015
  • The main purpose of the study is to explore the implications of Recognition Paradigm for domains of social work, especially focusing on the arguments exchanged between Axel Honneth and Nancy Fraser, two eminent scholars in this field. The Recognition paradigm, which is an alternative perspective in practical philosophy developed to cope with the changing socio-political situations in the late modern society, is providing the domain of social work with a lot of important theoretical and practical implications as well. In particular, Honneth's recognition theory which considers the recognition as a basic prototype in human development and construction of society is able to clarify the fundamental mission and territory the social work profession is to pursue. But for the meanwhile, Fraser's dual perspective of justice, which is an extended version of redistributive justice introducing the recognition component in it, can suggest diverse practical strategies to confront complex injustice-making structures effectively in the later modern society. In spite of these abundant implications in both theoretical and practical areas, the recognition paradigm still save several fundamental considerations for social work, such as the real meaning of the recognition in social work, the exact population from whom social work seek to get recognition, and the adequate strategy, so-called "recognition struggle" which social work is to employ to acquire the recognition.

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Alexithymia and the Recognition of Facial Emotion in Schizophrenic Patients (정신분열병 환자에서의 감정표현불능증과 얼굴정서인식결핍)

  • Noh, Jin-Chan;Park, Sung-Hyouk;Kim, Kyung-Hee;Kim, So-Yul;Shin, Sung-Woong;Lee, Koun-Seok
    • Korean Journal of Biological Psychiatry
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    • v.18 no.4
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    • pp.239-244
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    • 2011
  • Objectives Schizophrenic patients have been shown to be impaired in both emotional self-awareness and recognition of others' facial emotions. Alexithymia refers to the deficits in emotional self-awareness. The relationship between alexithymia and recognition of others' facial emotions needs to be explored to better understand the characteristics of emotional deficits in schizophrenic patients. Methods Thirty control subjects and 31 schizophrenic patients completed the Toronto Alexithymia Scale-20-Korean version (TAS-20K) and facial emotion recognition task. The stimuli in facial emotion recognition task consist of 6 emotions (happiness, sadness, anger, fear, disgust, and neutral). Recognition accuracy was calculated within each emotion category. Correlations between TAS-20K and recognition accuracy were analyzed. Results The schizophrenic patients showed higher TAS-20K scores and lower recognition accuracy compared with the control subjects. The schizophrenic patients did not demonstrate any significant correlations between TAS-20K and recognition accuracy, unlike the control subjects. Conclusions The data suggest that, although schizophrenia may impair both emotional self-awareness and recognition of others' facial emotions, the degrees of deficit can be different between emotional self-awareness and recognition of others' facial emotions. This indicates that the emotional deficits in schizophrenia may assume more complex features.