• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1766-1784
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• 2014

Finger vein images contain rich oriented features. Local line binary pattern (LLBP) is a good oriented feature representation method extended from local binary pattern (LBP), but it is limited in that it can only extract horizontal and vertical line patterns, so effective information in an image may not be exploited and fully utilized. In this paper, an orientation-selectable LLBP method, called generalized local line binary pattern (GLLBP), is proposed for finger vein recognition. GLLBP extends LLBP for line pattern extraction into any orientation. To effectually improve the matching accuracy, the soft power metric is employed to calculate the matching score. Furthermore, to fully utilize the oriented features in an image, the matching scores from the line patterns with the best discriminative ability are fused using the Hamacher rule to achieve the final matching score for the last recognition. Experimental results on our database, MMCBNU_6000, show that the proposed method performs much better than state-of-the-art algorithms that use the oriented features and local features, such as LBP, LLBP, Gabor filter, steerable filter and local direction code (LDC).

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.2
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• pp.51-60
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• 2020

Optical character recognition (OCR) has been studied for decades because it is very useful in a variety of places. Nowadays, OCR's performance has improved significantly due to outstanding deep learning technology. Thus, there is an increasing demand for commercial-grade but affordable OCR systems. We have developed a low-cost, high-performance OCR system for the industry with the cheapest embedded developer kit that supports GPU acceleration. To achieve high accuracy for industrial use on limited computing resources, we chose a state-of-the-art text recognition algorithm that uses an end-to-end deep learning network as a baseline model. The model was then improved by replacing the feature extraction network with the best one suited to our conditions. Among the various candidate networks, EfficientNet-B3 has shown the best performance: excellent recognition accuracy with relatively low memory consumption. Besides, we have optimized the model written in TensorFlow's Python API using TensorFlow-TensorRT integration and TensorFlow's C++ API, respectively.

• The Journal of the Acoustical Society of Korea
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• v.19 no.5
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• pp.9-12
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• 2000

Voice dialing system can recognize the speaker's command and dial the destinate phone number automatically. Such a system is useful for wireless handsets and portable communication devices. As a personal voice dialing system, all the commands are used to train the HMM for speech recognition based on owner-selected phrases. Its implementation requires much less memory space and computation resource compared to a speaker-independent system. Since only two or three training utterances per command are used in this system, it is difficult to estimate exact state duration distribution to improve the recognition performance. Therefore a post-processor is presented to improve the performance. Experiments which use the database collected through the telephone line showed that the proposed post-processor improves the recognition system performance.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.4 s.304
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• pp.35-44
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• 2005

Face recognition out of biometrics is considerable interesting due to high performance and accessibility in applications to security such as access control and banking service. Therefore, a study on the protocol of the performance test is an important issue to understand the art-of state and to show a direction in future works, in addtion to developing algorithms. We present a design criterion for the performance test protocol of face recognition system and show the result of experiment executed on identification and verification scenario based on PCA algorithm and XM2VTS DB

• 전자공학회논문지 IE
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• v.47 no.1
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• pp.12-21
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• 2010

In this paper, we suggest Location Recognition System using Sensor Network; it distinguishes locations. Furthermore, this paper proposes Intelligent Navigation System which presents the proper route for the user. INS evaluates the user's preference, tendency and environmental state using Sensor Network Module and current driving information. This system also uses Soft-computing method to learn and infer the person’s preference and tendency. This paper defines Intelligent Assistance Module (IAM) which is a connector in between a user and a robot; it is portable. All in all, we created a basic intelligent robot, Location Recognition System, and Environment Sensor Modules; we verified the proposed algorithm through computer simulations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.2
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• pp.200-205
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• 2006

Abstract This paper presents a personal identification method based on BMME and LDA for images acquired at anterior and posterior occlusion expression of teeth. The method consists of teeth region extraction, BMME, and pattern recognition forthe images acquired at the anterior and posterior occlusion state of teeth. Two occlusions can provide consistent teeth appearance in images and BMME can reduce matching error in pattern recognition. Using teeth images can be beneficial in recognition because teeth, rigid objects, cannot be deformed at the moment of image acquisition. In the experiments, the algorithm was successful in teeth recognition for personal identification for 20 people, which encouraged our method to be able to contribute to multi-modal authentication systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3599-3619
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• 2019

In human activity recognition system both static and motion information play crucial role for efficient and competitive results. Most of the existing methods are insufficient to extract video features and unable to investigate the level of contribution of both (Static and Motion) components. Our work highlights this problem and proposes Static-Motion fused features descriptor (SMFD), which intelligently leverages both static and motion features in the form of descriptor. First, static features are learned by two-stream 3D convolutional neural network. Second, trajectories are extracted by tracking key points and only those trajectories have been selected which are located in central region of the original video frame in order to to reduce irrelevant background trajectories as well computational complexity. Then, shape and motion descriptors are obtained along with key points by using SIFT flow. Next, cholesky transformation is introduced to fuse static and motion feature vectors to guarantee the equal contribution of all descriptors. Finally, Long Short-Term Memory (LSTM) network is utilized to discover long-term temporal dependencies and final prediction. To confirm the effectiveness of the proposed approach, extensive experiments have been conducted on three well-known datasets i.e. UCF101, HMDB51 and YouTube. Findings shows that the resulting recognition system is on par with state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.892-911
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• 2019

Biometric recognition systems have been widely used for information security. Among the most popular biometric traits, there are fingerprint and face due to their high recognition accuracies. However, the security system that uses face recognition as the login method are vulnerable to face-spoofing attacks, from using printed photo or video of the valid user. In this study, we propose a fast and robust method to detect face-spoofing attacks based on the analysis of spatial frequency differences between the real and fake videos. We found that the effect of a spoofing attack stands out more prominently in certain regions of the 2D Fourier spectra and, therefore, it is adequate to use the information about those regions to classify the input video or image as real or fake. We adopt a divide-conquer-aggregate approach, where we first divide the frequency domain image into local blocks, classify each local block independently, and then aggregate all the classification results by the weighted-sum approach. The effectiveness of the methodology is demonstrated using two different publicly available databases, namely: 1) Replay Attack Database and 2) CASIA-Face Anti-Spoofing Database. Experimental results show that the proposed method provides state-of-the-art performance by processing fewer frames of each video.

• Genomics & Informatics
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• v.17 no.2
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• pp.15.1-15.7
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• 2019

Automatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases, adverse reactions or symptoms. The identification of drug mentions is also a prior step for complex event types such as drug dosage recognition, duration of medical treatments or drug repurposing. Formally, this task is known as named entity recognition (NER), meaning automatically identifying mentions of predefined entities of interest in running text. In the domain of medical texts, for chemical entity recognition (CER), techniques based on hand-crafted rules and graph-based models can provide adequate performance. In the recent years, the field of natural language processing has mainly pivoted to deep learning and state-of-the-art results for most tasks involving natural language are usually obtained with artificial neural networks. Competitive resources for drug name recognition in English medical texts are already available and heavily used, while for other languages such as Spanish these tools, although clearly needed were missing. In this work, we adapt an existing neural NER system, NeuroNER, to the particular domain of Spanish clinical case texts, and extend the neural network to be able to take into account additional features apart from the plain text. NeuroNER can be considered a competitive baseline system for Spanish drug and CER promoted by the Spanish national plan for the advancement of language technologies (Plan TL).

• The Korean Journal of Health Service Management
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• v.13 no.1
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• pp.31-42
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• 2019

Objectives: The purpose of this study is to analyze the relationship between the change gap in the perception of subjective hierarchy and medical expenditure and the factors influencing medical expenditure. Methods: An analysis based on the the data extracted from the Panel Study of Korea Health Panel for 2012-2013 (n=9,359) is conducted. Further in this study, data analysis included a chi-square test and logistic regression using SPSS version. 22.0 to analyze the factors influencing the turnover intention of industrial workers. Results: Model I showed decreases in medical expenditure by 1.247, 1.391, and 1.441 times in social classes one, two, and Model II showed an increase in medical expenditure by age, spouse, number of family members living together, insurance type, income class, economic activities, subjective health status, chronic illness and change on subjective recognition of social class. Conclusions: The study concludes that the state and community require psychological, social, and cultural support, in addition to individual efforts, to reduce medical expenditure.