The Experts below are selected from a list of 9234 Experts worldwide ranked by ideXlab platform
Jongsoo Choi - One of the best experts on this subject based on the ideXlab platform.
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Facial component detection for efficient Facial Characteristic point extraction
International Conference on Image Analysis and Recognition, 2005Co-Authors: Dongwook Kim, Jintae Kim, Yongin Yoon, Jongsoo ChoiAbstract:This paper proposes an algorithm detecting Facial component to efficiently extract the FCP (Facial Characteristic Point). The FCP plays an important role in Facial expression representation or recognition. For efficient FCP extraction using image processing, we analyze and improve the conventional algorithms detecting Facial components that are the basis of the FCP extraction. The proposed algorithm includes face region detection without the effect of skin-color hair, eye region detection with weighted template, eyebrow region detection using a modified histogram, and mouth region detection using skin Characteristics.
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ICIAR - Facial component detection for efficient Facial Characteristic point extraction
Lecture Notes in Computer Science, 2005Co-Authors: Dongwook Kim, Jintae Kim, Yongin Yoon, Jongsoo ChoiAbstract:This paper proposes an algorithm detecting Facial component to efficiently extract the FCP (Facial Characteristic Point). The FCP plays an important role in Facial expression representation or recognition. For efficient FCP extraction using image processing, we analyze and improve the conventional algorithms detecting Facial components that are the basis of the FCP extraction. The proposed algorithm includes face region detection without the effect of skin-color hair, eye region detection with weighted template, eyebrow region detection using a modified histogram, and mouth region detection using skin Characteristics.
Y. Y. Jiang - One of the best experts on this subject based on the ideXlab platform.
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correlation between hyoid bone position and airway dimensions in chinese adolescents by cone beam computed tomography analysis
International Journal of Oral and Maxillofacial Surgery, 2016Co-Authors: Y. Y. JiangAbstract:Abstract This study aimed to investigate the correlation between upper airway dimensions and hyoid bone position in Chinese adolescents based on cone beam computed tomography (CBCT) images. CBCT images from a total of 254 study subjects were included. The upper airway and hyoid bone parameters were measured by Materialism's interactive medical image control system (MIMICS) v.16.01 (Materialise, Leuven, Belgium). The airway dimensions were evaluated in terms of volume, cross-sectional area (CSA), mean CSA, length, anteroposterior dimension of the cross-section (AP), lateral dimension of the cross-section (LAT), and LAT/AP ratio. The hyoid bone position was evaluated using eight linear parameters and two angular parameters. Facial Characteristics were evaluated using three linear parameters and three angular parameters. Most hyoid bone position parameters (especially the distance between the hyoid bone and hard palate) were significantly associated with most airway dimension parameters. Significant correlations were also observed between the different Facial Characteristic parameters and hyoid bone position parameters. Most airway dimension parameters showed significant correlations with linear Facial parameters, but they displayed significant correlations with only a few angular Facial parameters. These findings provide an understanding of the static relationship between the hyoid bone position and airway dimensions, which may serve as a reference for surgeons before orthodontic or orthognathic surgery.
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Correlation between hyoid bone position and airway dimensions
2016Co-Authors: Y. Y. JiangAbstract:This study aimed to investigate the correlation between upper airway dimensions and hyoid bone position in Chinese adolescents based on cone beam computed tomography (CBCT) images. CBCT images from a total of 254 study subjects were included. The upper airway and hyoid bone parameters were measured by Materialism's interactive medical image control system (MIMICS) v.16.01 (Materialise, Leuven, Belgium). The airway dimensions were evaluated in terms of volume, cross-sectional area (CSA), mean CSA, length, anteroposterior dimension of the cross-section (AP), lateral dimension of the cross-section (LAT), and LAT/AP ratio. The hyoid bone position was evaluated using eight linear parameters and two angular parameters. Facial Characteristics were evaluated using three linear parameters and three angular parameters. Most hyoid bone position parameters (especially the distance between the hyoid bone and hard palate) were significantly associated with most airway dimension parameters. Significant correlations were also observed between the different Facial Characteristic parameters and hyoid bone position parameters. Most airway dimension parameters showed significant correlations with linear Facial parameters, but they displayed significant correlations with only a few angular Facial parameters. These findings provide an understanding of the static relationship between the hyoid bone position and airway dimensions, which may serve as a reference for surgeons before orthodontic or orthognathic surgery.
Dongwook Kim - One of the best experts on this subject based on the ideXlab platform.
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Facial component detection for efficient Facial Characteristic point extraction
International Conference on Image Analysis and Recognition, 2005Co-Authors: Dongwook Kim, Jintae Kim, Yongin Yoon, Jongsoo ChoiAbstract:This paper proposes an algorithm detecting Facial component to efficiently extract the FCP (Facial Characteristic Point). The FCP plays an important role in Facial expression representation or recognition. For efficient FCP extraction using image processing, we analyze and improve the conventional algorithms detecting Facial components that are the basis of the FCP extraction. The proposed algorithm includes face region detection without the effect of skin-color hair, eye region detection with weighted template, eyebrow region detection using a modified histogram, and mouth region detection using skin Characteristics.
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ICIAR - Facial component detection for efficient Facial Characteristic point extraction
Lecture Notes in Computer Science, 2005Co-Authors: Dongwook Kim, Jintae Kim, Yongin Yoon, Jongsoo ChoiAbstract:This paper proposes an algorithm detecting Facial component to efficiently extract the FCP (Facial Characteristic Point). The FCP plays an important role in Facial expression representation or recognition. For efficient FCP extraction using image processing, we analyze and improve the conventional algorithms detecting Facial components that are the basis of the FCP extraction. The proposed algorithm includes face region detection without the effect of skin-color hair, eye region detection with weighted template, eyebrow region detection using a modified histogram, and mouth region detection using skin Characteristics.
Jintae Kim - One of the best experts on this subject based on the ideXlab platform.
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Facial component detection for efficient Facial Characteristic point extraction
International Conference on Image Analysis and Recognition, 2005Co-Authors: Dongwook Kim, Jintae Kim, Yongin Yoon, Jongsoo ChoiAbstract:This paper proposes an algorithm detecting Facial component to efficiently extract the FCP (Facial Characteristic Point). The FCP plays an important role in Facial expression representation or recognition. For efficient FCP extraction using image processing, we analyze and improve the conventional algorithms detecting Facial components that are the basis of the FCP extraction. The proposed algorithm includes face region detection without the effect of skin-color hair, eye region detection with weighted template, eyebrow region detection using a modified histogram, and mouth region detection using skin Characteristics.
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Facial Characteristic point extraction for representation of Facial expression
The Journal of the Korean Institute of Information and Communication Engineering, 2005Co-Authors: Jintae KimAbstract:This paper proposes an algorithm for Facial Characteristic Point(FCP) extraction. The FCP plays an important role in expression representation for face animation, avatar mimic or Facial expression recognition. Conventional algorithms extract the FCP with an expensive motion capture device or by using markers, which give an inconvenience or a psychological load to experimental person. However, the proposed algorithm solves the problems by using only image processing. For the efficient FCP extraction, we analyze and improve the conventional algorithms detecting Facial components, which are basis of the FCP extraction.
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ICIAR - Facial component detection for efficient Facial Characteristic point extraction
Lecture Notes in Computer Science, 2005Co-Authors: Dongwook Kim, Jintae Kim, Yongin Yoon, Jongsoo ChoiAbstract:This paper proposes an algorithm detecting Facial component to efficiently extract the FCP (Facial Characteristic Point). The FCP plays an important role in Facial expression representation or recognition. For efficient FCP extraction using image processing, we analyze and improve the conventional algorithms detecting Facial components that are the basis of the FCP extraction. The proposed algorithm includes face region detection without the effect of skin-color hair, eye region detection with weighted template, eyebrow region detection using a modified histogram, and mouth region detection using skin Characteristics.
Nazil Perveen - One of the best experts on this subject based on the ideXlab platform.
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Facial Expression Recognition System Using Facial Characteristic Points And ID3
International Journal of Computer and Communication Technology, 2014Co-Authors: Shubhrata Gupta, Keshri Verma, Nazil PerveenAbstract:Facial expression is one of the most powerful, natural, and abrupt means for human beings which have the knack to communicate emotion and regulate inter-personal behaviour. In this paper we present a novel approach for Facial expression detection using decision tree. Facial expression information is mostly concentrate on Facial expression information regions, so the mouth, eye and eyebrow regions are segmented from the Facial expression images firstly. Using these templates we calculate 30 Facial Characteristics points (FCP’s). These Facial Characteristic points describe the position and shape of the above three organs to find diverse parameters which are input to the decision tree for recognizing different Facial expressions.
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Facial expression recognition using Facial Characteristic points and Gini index
2012 Students Conference on Engineering and Systems, 2012Co-Authors: Nazil Perveen, Shubhrata Gupta, Kesari VermaAbstract:In this paper, Facial Expression Recognition (FER) system, aid in modeling Facial expression space for Facial expression recognition by Facial Characteristic points and Gini index. Facial expression information are mostly concentrated on Facial expression information regions, so the, eyes, eyebrows, and mouth are extracted from the input image. When a face image is input the feature extraction is performed which help in detecting Facial Characteristic points. Facial animation parameters are calculated in order to recognize one of the six basic Facial expressions. The proposed technique is applied to JAFFE database consisting 30 images, each having six basic Facial expression images (neutral, happy, surprise, fear, sad and angry). For certain expressions like, surprise, fear and happy it gives the promising results with best recognition rate, however for expressions like, neutral, sad and angry the recognition rate is lower. The proposed technique is implemented in MATLAB version 7.8.0.347 (R2009a).
