Bon Woo Hwang

A full-body gesture database for automatic gesture recognition

This paper presents a full-body gesture database which contains 2D video data and 3D motion data of 14 normal gestures, 10 abnormal gestures and 30 command gestures for 20 subjects. We call this database the Korea University Gesture (KUG) database. Using 3D motion cameras and 3 sets of stereo cameras, we captured 3D motion data and 3 pairs of stereo-video data at 3 different directions for normal and abnormal gestures. In case of command gestures, 2 pairs of stereo-video data is obtained by 2 sets of stereo cameras with different focal length in order to effectively capture views of whole body and upper body, simultaneously. In addition to these, the 2D silhouette data is synthesized by separating a subject and background in 2D stereo-video data and saved as binary mask images. In this paper, we describe the gesture capture system, the organization of database, the potential usages of the database and the way of obtaining the KUG database

Face reconstruction from a small number of feature points

This paper proposes a method for face reconstruction that makes use of only a small set of feature points. Faces can be modeled by forming linear combinations of prototypes of shape and texture information. With the shape and future information at the feature points alone, we can achieve only an approximation to the deformation required. In such an underdetermined condition, we find an optimal solution using a simple least square minimization method. As experimental results, we show well-reconstructed 2D faces even from a small number of feature points.

Authenticating corrupted face image based on noise model

In this paper, we propose a method for authenticating corrupted face images based on noise model. The proposed method first generates corrupteed images by controlling nois parameters in the training phase. The corrupted images and noise parameters are represented by a linear combination of prototypes of the corrupted images and the noise parameters. With the corrupted image and an original image, we can estimate noise parameters of the corrupted face image in the testing phase. Then, we can make a synthesized face image from the original face image with the estimated noise parameters and verify it with the corrupted face image. Our experimental results show that the proposed method can estimate noise parameters accurately and improve the performance of face authentication.

Performance evaluation of face recognition algorithms on Asian face database

Human face is one of the most common and useful keys to a persons identity. Although, a number of face recognition algorithms have been proposed, many researchers believe that the technology should be improved further in order to overcome the instability due to variable illuminations, expressions, poses and accessories. In general, face databases for European and American such as CMU PIE (USA), FERET (USA), AR Face DB (USA) and XM2VTS (UK) have been used for training face recognition algorithms and testing the performance of those. However, many of the images in databases are not adequately annotated with the exact pose angle, illumination angle and illuminant color. Also, the faces on these databases have definitely different characteristics from those of Asian. Thus, we constructed the well-designed Korean face database (KFDB), which includes not only images but also ground truth information for facial feature points, and description files for subjects and exact capture environments. In this paper, we report the experimental results of face recognition performed using CM (correlation matching), PCA (principal component analysis) and LFA (local feature analysis) algorithms under various conditions on the KFDB.

2D and 3d full-body gesture database for analyzing daily human gestures

This paper presents a database of 14 representative gestures in daily life of 20 subjects. We call this database the 2D and 3D Full-Body Gesture (FBG) database. Using 12 sets of 3D motion cameras and 3 sets of stereo cameras, we captured 3D motion data and 3 pairs of stereo-video data at 3 different directions for each gesture. In addition to these, the 2D silhouette data is synthesized by separating a subject and background in 2D stereo-video data and saved as binary mask images. In this paper, we describe the gesture capture system, the organization of database, the potential usages of the database and the way of obtaining the FBG database. We expect that this database would be very useful for the study of 2D/3D human gestures.

Retrieval of the top N matches with support vector machines

Support vector machines (SVM) have been recently proposed for pattern recognition. Their basic property allows us to find a decision surface between two classes in terms of a hyperplane in a high dimensional space. In a multiclass recognition problem, SVM are used in the form of a combination of binary classifiers. However, SVM are unable to retrieve the top N matches, since they are designed to yield only one-the best match-in a multiclass problem. In other words, there is no proper similarity measurement for ordering all the classes in a given space using SVM. In this paper, we present an efficient method for the retrieval of the top N matches in a multiclass problem using SVM. For evaluation of the proposed method, we compared its result with that of a PCA algorithm in ranking the matches between classes.

A FULL-BODY GESTURE DATABASE FOR HUMAN GESTURE ANALYSIS

This paper presents a full-body gesture database which contains 2D video data and 3D motion data of 14 normal gestures, 10 abnormal gestures and 30 command gestures for 20 subjects. We call this database the Korea University Gesture (KUG) database. Using 3D motion cameras and 3 sets of stereo cameras, we captured 3D motion data and 3 pairs of stereo-video data in 3 different directions for normal and abnormal gestures. In case of command gestures, 2 pairs of stereo-video data were obtained by 2 sets of stereo cameras with different focal lengths in order to capture views of whole body and upper body, simultaneously. The 2D silhouette data was synthesized by separating a subject and background in 2D stereo-video data. In this paper, we describe the gesture capture system, the organization of database, the potential usages of the database and the contact point for the KUG database. We expect that this database would be very useful for the study of 2D/3D human gesture and its application.

Facial image reconstruction by SVDD-Based pattern de-noising

The SVDD (support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. In this paper, we consider the problem of reconstructing facial images from the partially damaged ones, and propose to use the SVDD-based de-noising for the reconstruction. In the proposed method, we deal with the shape and texture information separately. We first solve the SVDD problem for the data belonging to the given prototype facial images, and model the data region for the normal faces as the ball resulting from the SVDD problem. Next, for each damaged input facial image, we project its feature vector onto the decision boundary of the SVDD ball so that it can be tailored enough to belong to the normal region. Finally, we obtain the image of the reconstructed face by obtaining the pre-image of the projection, and then further processing with its shape and texture information. The applicability of the proposed method is illustrated via some experiments dealing with damaged facial images.

Face Reconstruction Using a Small Set of Feature Points

This paper proposes a method for face reconstruction that makes use of only a small set of feature points. Faces can be modeled by forming linear combinations of prototypes of shape and texture information. With the shape and texture information at the feature points alone, we can achieve only an approximation to the deformation required. In such an under-determined condition, we find an optimal solution using a simple least square minimization method. As experimental results, we show well-reconstructed 2D faces even from a small number of feature points.

Authenticating corrupted photo images based on noise parameter estimation

Photo image authentication is an interesting and demanding field in the computer vision and image processing community. This research is motivated by its wide range of applications, which include smart card authentication systems, biometric passport systems, etc. In this paper, we propose a method of authenticating corrupted photo images based on noise parameter estimation. The proposed method first generates corrupted images by adjusting the noise parameters in the initial training phase. This set of corrupted images and the noise parameters can be represented by a linear combination of the prototypes of the corrupted images and the noise parameters. In the testing phase, the noise parameters of the corrupted photo image can be estimated with a corrupted image and an original image. Finally, we can make a synthesized photo image from the original photo image using the estimated noise parameters and verify it with the corrupted photo image. The experimental results show that the proposed method can estimate the noise parameters accurately and improve the performance of photo image authentication.