• Journal of KIISE:Software and Applications
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• v.31 no.1
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• pp.54-60
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• 2004

Face recognition is one of the problems to be solved by appearance based matching technique. However, the appearance of face image is very sensitive to variation in illumination. One of the easiest ways for better performance is to collect more training samples acquired under variable lightings but it is not practical in real world. ]:n object recognition, it is desirable to focus on feature extraction or normalization technique rather than focus on classifier. This paper presents a simple approach to normalization of faces subject to directional illumination. This is one of the significant issues that cause error in the face recognition process. The proposed method, ICR(illumination Compensation based on Multiple Linear Regression), is to find the plane that best fits the intensity distribution of the face image using the multiple linear regression, then use this plane to normalize the face image. The advantages of our method are simple and practical. The planar approximation of a face image is mathematically defined by the simple linear model. We provide experimental results to demonstrate the performance of the proposed ICR method on public face databases and our database. The experimental results show a significant improvement of the recognition accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.683-687
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• 2010

This paper studied some methods which use frequency warping method that is the one of the speech transformation method to develope the robust speech recognition system for the emotional variation. For this purpose, the effect of emotional variations on the speech signal were studied using speech database containing various emotions and it is observed that speech spectrum is affected by the emotional variation and this effect is one of the reasons that makes the performance of the speech recognition system worse. In this paper, new training method that uses frequency warping in training process is presented to reduce the effect of emotional variation and the speech recognition system based on vocal tract length normalization method is developed to be compared with proposed system. Experimental results from the isolated word recognition using HMM showed that new training method reduced the error rate of the conventional recognition system using speech signal containing various emotions.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.6 s.312
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• pp.17-25
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• 2006

As information systems to be computerized get significantly scaled up, data modeling issues apparently considered to be crucial once again as the early 1980's under the terms of data governance, data architecture or data quality. Unfortuately, merely resorting to heuristics-based field approaches with more or less no firm theoretical foundation of knowledge with regard to criteria of data design lead quite often to major failures in efficacy of data modeling. In this paper, we have compared normalization-critical data modeling approach, well-known as the Non-Stop Data Modeling methodology in the literature, to the Information Engineering in which in many occasions the notion of do-normalization is supported and even recommended as a mandatory part in its modeling nature. Quantitative analyses have revealed that NS methodology ostensibly outperforms IE methodology in terms of efficiency indices like adequacy of entity judgement, degree of existence of data circulation path that confirms the balancedness of data design and ratio of unnecessary data attribute replication.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.1
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• pp.25-44
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• 2010

With the increasing popularity of mobile devices, it has become necessary to protect private information and content in these devices. Face recognition has been favored over conventional passwords or security keys, because it can be easily implemented using a built-in camera, while providing user convenience. However, because mobile devices can be used both indoors and outdoors, there can be many illumination changes, which can reduce the accuracy of face recognition. Therefore, we propose a new face recognition method on a mobile device robust to illumination variations. This research makes the following four original contributions. First, we compared the performance of face recognition with illumination variations on mobile devices for several illumination normalization procedures suitable for mobile devices with low processing power. These include the Retinex filter, histogram equalization and histogram stretching. Second, we compared the performance for global and local methods of face recognition such as PCA (Principal Component Analysis), LNMF (Local Non-negative Matrix Factorization) and LBP (Local Binary Pattern) using an integer-based kernel suitable for mobile devices having low processing power. Third, the characteristics of each method according to the illumination va iations are analyzed. Fourth, we use two matching scores for several methods of illumination normalization, Retinex and histogram stretching, which show the best and $2^{nd}$ best performances, respectively. These are used as the inputs of an SVM (Support Vector Machine) classifier, which can increase the accuracy of face recognition. Experimental results with two databases (data collected by a mobile device and the AR database) showed that the accuracy of face recognition achieved by the proposed method was superior to that of other methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.773-778
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• 2009

This paper studied the training methods less affected by the emotional variation for the development of the robust speech recognition system. For this purpose, the effect of emotional variations on the speech signal were studied using speech database containing various emotions. The performance of the speech recognition system trained by using the speech signal containing no emotion is deteriorated if the test speech signal contains the emotions because of the emotional difference between the test and training data. In this study, it is observed that vocal tract length of the speaker is affected by the emotional variation and this effect is one of the reasons that makes the performance of the speech recognition system worse. In this paper, vocal tract normalization method is used to develop the robust speech recognition system for emotional variations. Experimental results from the isolated word recognition using HMM showed that the vocal tract normalization method reduced the error rate of the conventional recognition system by 41.9% when emotional test data was used.

• The KIPS Transactions:PartD
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• v.14D no.6
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• pp.615-622
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• 2007

Recently, users who want to be provided motile devices, such as PDP, PMP, and IPTV connected wireless internet, with multimedia contents are increasing due to an influence of multimedia contents. In the paper, we propose an efficient database design method for managing mobile multimedia services on home network systems. For this, we build relations using attributes required while providing multimedia services, and then design a database. Specially, we propose a database design method based on normalization theory to eliminate redundancies and update anomalies caused by a non trivial multi valued dependency in relations. In the experiments, we compare and analyze occurrence frequencies of data redundancies and update anomalies through query executions on the relation decomposed into normal forms. The results reveal that our database design is failrly effective.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.199-206
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• 2023

The current software becomes the huge size of source codes. Therefore it is increasing the importance and necessity of static analysis for high-quality product. With static analysis of the code, it needs to identify the defect and complexity of the code. Through visualizing these problems, we make it guild for developers and stakeholders to understand these problems in the source codes. Our previous visualization research focused only on the process of storing information of the results of static analysis into the Database tables, querying the calculations for quality indicators (CK Metrics, Coupling, Number of function calls, Bad-smell), and then finally visualizing the extracted information. This approach has some limitations in that it takes a lot of time and space to analyze a code using information extracted from it through static analysis. That is since the tables are not normalized, it may occur to spend space and time when the tables(classes, functions, attributes, Etc.) are joined to extract information inside the code. To solve these problems, we propose a regularized design of the database tables, an extraction mechanism for quality metric indicators inside the code, and then a visualization with the extracted quality indicators on the code. Through this mechanism, we expect that the code visualization process will be optimized and that developers will be able to guide the modules that need refactoring. In the future, we will conduct learning of some parts of this process.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.357-362
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• 2005

Iris recognition system is the one of the most reliable biometries recognition system. An algorithm is proposed to determine the localized iris from the iris image received from iris input camera in client. For the first step, the algorithm determines the center of pupil. For the second step, the algorithm determines the outer boundary of the iris and the pupillary boundary. The localized iris area is transformed into polar coordinates. After performing three times Wavelet transformation, normalization was done using a sigmoid function. The converting binary process performs normalized value of pixel from 0 to 255 to be binary value, and then the converting binary process is compared pairs of two adjacent pixels. The binary code of the iris is transmitted to the server by the network. In the server, the comparing process compares the binary value of presented iris to the reference value in the database. The process of recognition or rejection is dependent on the value of Hamming Distance. After matching the binary value of presented iris with the database stored in the server, the result is transmitted to the client.

• The Korean Journal of Archival Studies
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• no.34
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• pp.57-105
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• 2012

The CAMS(Central Archives Management System) of NAK(National Archives of Korea) is an important system which receives and manages large amount of electronic records annually from 2015. From the point of view in database design, this paper analyzes the database schema of CAMS and discusses the direction of overall improvement of the CAMS. Firstly this research analyzes the tables for records and folders in the CAMS database which are core tables for the electronic records management. As a result, researchers notice that it is difficult to trust the quality of the records in the CAMS, because two core tables are entirely not normalized and have many columns whose roles are unknown. Secondly, this study suggests directions of normalization for the tables for records and folders in the CAMS database like followings: First, redistributing the columns into proper tables to reduce the duplication. Second, separating the columns about the classification scheme into separate tables. Third, separating the columns about the records types and sorts into separate tables. Lastly, separating metadata information related to the acquisition, takeover and preservation into separate tables. Thirdly, this paper suggests considerations to design and manage the database schema in each phase of archival management. In the ingest phase, the system should be able to process large amount of records as batch jobs in time annually. In the preservation phase, the system should be able to keep the management histories in the CAMS as audit trails including the reclassification, revaluation, and preservation activities related to the records. In the access phase, the descriptive metadata sets for the access should be selected and confirmed in various ways. Lastly, this research also shows the prototype of conceptual database schema for the CAMS which fulfills the metadata standards for records.

• Journal of Information Processing Systems
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• v.7 no.3
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• pp.425-434
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• 2011

The success of iris recognition depends mainly on two factors: image acquisition and an iris recognition algorithm. In this study, we present a system that considers both factors and focuses on the latter. The proposed algorithm aims to find out the most efficient wavelet family and its coefficients for encoding the iris template of the experiment samples. The algorithm implemented in software performs segmentation, normalization, feature encoding, data storage, and matching. By using the Haar and Biorthogonal wavelet families at various levels feature encoding is performed by decomposing the normalized iris image. The vertical coefficient is encoded into the iris template and is stored in the database. The performance of the system is evaluated by using the number of degrees of freedom, False Reject Rate (FRR), False Accept Rate (FAR), and Equal Error Rate (EER) and the metrics show that the proposed algorithm can be employed for an iris recognition system.