• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.435-448
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• 2008

3D posture recognition is a solution to overcome the limitation of 2D posture recognition. There are many researches carried out for 3D posture recognition using 3D data. The 3D data consist of massive surface points which are rich of information. However, it is difficult to extract the important features for posture recognition purpose. Meanwhile, it also consumes lots of processing time. In this paper, we introduced a dimension reduction method that transform 3D surface points of an object to 2D data representation in order to overcome the issues of feature extraction and time complexity of 3D posture recognition. For a better feature extraction and matching process, a cylindrical boundary is introduced in meshless parameterization, its offer a fast processing speed of dimension reduction process and the output result is applicable for recognition purpose. The proposed approach is applied to hand and human posture recognition in order to verify the efficiency of the feature extraction.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.42-53
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• 2012

In pattern recognition and machine learning society, classification has been a classical problem and the most widely researched area. Adaptive boosting also known as Adaboost has been successfully applied to binary classification problems. It is a kind of boosting algorithm capable of constructing a strong classifier through a weighted combination of weak classifiers. On the other hand, the PCA and LDA algorithms are the most popular linear feature extraction methods used mainly for dimensionality reduction. In this paper, the combination of Adaboost and feature extraction methods is proposed for efficient classification of two class data. Conventionally, in classification problems, the roles of feature extraction and classification have been distinct, i.e., a feature extraction method and a classifier are applied sequentially to classify input variable into several categories. In this paper, these two steps are combined into one resulting in a good classification performance. More specifically, each projection vector is treated as a weak classifier in Adaboost algorithm to constitute a strong classifier for binary classification problems. The proposed algorithm is applied to UCI dataset and FRGC dataset and showed better recognition rates than sequential application of feature extraction and classification methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.506-511
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• 2004

Biometrics is getting more and more attention in recent years for security and other concerns. So far, only fingerprint recognition has seen limited success for on-line security check, since other biometrics verification and identification systems require more complicated and expensive acquisition interfaces and recognition processes. Hand-Geometry has been used for biometric verification and identification because of its acquisition convenience and good performance for verification and identification performance. Hence, it can be a good candidate for online checks. Therefore, this paper proposes a Hand-Geometry recognition system based on geometrical features of hand. From anatomical point of view, human hand can be characterized by its length, width, thickness, geometrical composition, shapes of the palm, and shape and geometry of the fingers. This paper proposes thirty relevant features for a Hand-Geometry recognition system. However, during experimentation, it was discovered that length measured from the tip of the finger was not a reliable feature. Hence, we propose a new technique based on Genetic Algorithm for extraction of the center of nail bottom, in order to use it for the length feature.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.4
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• pp.309-318
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• 2001

In this paper, we describe methods that analyze a human gesture. A human interface(HI) system for analyzing gesture extracts the head and hand regions after taking image sequence of and operators continuous behavior using CCD cameras. As gestures are accomplished with operators head and hands motion, we extract the head and hand regions to analyze gestures and calculate geometrical information of extracted skin regions. The analysis of head motion is possible by obtaining the face direction. We assume that head is ellipsoid with 3D coordinates to locate the face features likes eyes, nose and mouth on its surface. If was know the center of feature points, the angle of the center in the ellipsoid is the direction of the face. The hand region obtained from preprocessing is able to include hands as well as arms. For extracting only the hand region from preprocessing, we should find the wrist line to divide the hand and arm regions. After distinguishing the hand region by the wrist line, we model the hand region as an ellipse for the analysis of hand data. Also, the finger part is represented as a long and narrow shape. We extract hand information such as size, position, and shape.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.319-326
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• 2014

According to the increasing mobile security users who have experienced authentication failure by forgetting passwords, user names, or a response to a knowledge-based question have preference for biological information such as hand geometry, fingerprints, voice in personal identification and authentication. Therefore biometric verification of personal identification and authentication for mobile security provides assurance to both the customer and the seller in the internet. Our study focuses on human hand biometric information recognition system for personal identification and personal Authentication, including its shape, palm features and the lengths and widths of the fingers taken from mobile phone photographs such as iPhone4 and galaxy s2. Our hand biometric information recognition system consists of six steps processing: image acquisition, preprocessing, removing noises, extracting standard hand feature extraction, individual feature pattern extraction, hand biometric information recognition for personal identification and authentication from input images. The validity of the proposed system from mobile phone image is demonstrated through 93.5% of the sucessful recognition rate for 250 experimental data of hand shape images and palm information images from 50 subjects.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.4
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• pp.328-336
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• 2012

In this paper, we propose a robust fingertip extraction and extended Continuously Adaptive Mean Shift (CAMSHIFT) based robust hand gesture recognition for natural human-like HRI (Human-Robot Interaction). Firstly, for efficient and rapid hand detection, the hand candidate regions are segmented by the combination with robust $YC_bC_r$ skin color model and haar-like features based adaboost. Using the extracted hand candidate regions, we estimate the palm region and fingertip position from distance transformation based voting and geometrical feature of hands. From the hand orientation and palm center position, we find the optimal fingertip position and its orientation. Then using extended CAMSHIFT, we reliably track the 2D hand gesture trajectory with extracted fingertip. Finally, we applied the conditional density propagation (CONDENSATION) to recognize the pre-defined temporal motion trajectories. Experimental results show that the proposed algorithm not only rapidly extracts the hand region with accurately extracted fingertip and its angle but also robustly tracks the hand under different illumination, size and rotation conditions. Using these results, we successfully recognize the multiple hand gestures.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.229-235
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• 1996

CAD diagrams generally consists of many basic components: symbols, character, and connection lines. Thus, to recognize the diagrams, it is necessary to extract each components, and understand their meanings and relation among them. This paper describes a method for linking basic components extracted efficiently from hand-down diagrams to OrCAD data format. Experimental results with a hand-drawn diagrams of electronic and logic circuit show utility of the proposed method.

• ETRI Journal
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• v.41 no.6
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• pp.750-759
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• 2019

Presently, the extraction of hand-crafted features is still the dominant method in radar emitter recognition. To solve the complicated problems of selection and updation of empirical features, we present a novel automatic feature extraction structure based on deep learning. In particular, a convolutional neural network (CNN) is adopted to extract high-level abstract representations from the time-frequency images of emitter signals. Thus, the redundant process of designing discriminative features can be avoided. Furthermore, to address the performance degradation of a single platform, we propose the construction of an ensemble learning-based architecture for multi-platform fusion recognition. Experimental results indicate that the proposed algorithms are feasible and effective, and they outperform other typical feature extraction and fusion recognition methods in terms of accuracy. Moreover, the proposed structure could be extended to other prevalent ensemble learning alternatives.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3924-3940
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• 2020

Dynamic hand gesture recognition(d-HGR) plays an important role in human-computer interaction(HCI) system. With the growth of hand-pose estimation as well as 3D depth sensors, depth, and the hand-skeleton dataset is proposed to bring much research in depth and 3D hand skeleton approaches. However, it is still a challenging problem due to the low resolution, higher complexity, and self-occlusion. In this paper, we propose a hand-shape feature extraction(HSFE) network to produce robust hand-shapes. We build a hand-shape model, and hand-skeleton based on LSTM to exploit the temporal information from hand-shape and motion changes. Fusion between two models brings the best accuracy in dynamic hand gesture (DHG) dataset.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.302-306
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• 1994

This paper describes how to recognize hand written Hangeul character using the stroke order of the elementary segment. The recognition system is constructed of parts : character input part, segment disassembling part, character element extraction part and character recognition part. The character input part reads the character and performs thinning algorithm. In the segment disassembling part, the input character is disassembled into elementary segments using the direction codes and the feature parameters. In the character element extraction part, we extract the character element using the stroke order and the knowledge rule. Finally, we able to recognize the hand written Hangeul characters by assembling the character elements, in the character recognition part.