• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1501-1514
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• 2008

The face shape extracted by the depth values has different appearance as the most important facial information. The face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by multiple frequency domains for each depth image using the modified fuzzy c-mean algorithm. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. And the second step takes into consideration of the orientated frontal posture to normalize. Multiple contour line areas which have a different shape for each person are extracted by the depth threshold values from the reference point, nose tip. And then, the frequency component extracted from the wavelet subband can be adopted as feature information for the authentication problems. The third step of approach concerns the application of eigenface to reduce the dimension. And the linear discriminant analysis (LDA) method to improve the classification ability between the similar features is adapted. In the last step, the individual classifiers using the modified fuzzy c-mean method based on the K-NN to initialize the membership degree is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) showed the highest recognition rate among the extracted regions, and the proposed classification method achieved 98.3% recognition rate, incase of fuzzy cluster.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.10
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• pp.421-428
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• 2017

The need for various approaches to non-face-to-face identification technology for registering and authenticating users online is being required because of the growth of online financial services and the rapid development of financial technology. In general, non-face-to-face approaches can be exposed to a greater number of threats than face-to-face approaches. Therefore, identification policies and technologies to verify users by using various factors and channels are being studied in order to complement the risks and to be more reliable non-face-to-face identification methods. One of these new approaches is to collect and verify a large number of personal information of user. Therefore, we propose a big-data based non-face-to-face Identity Proofing method that verifies identity on online based on various and large amount of information of user. The proposed method also provides an identification information management scheme that collects and verifies only the user information required for the identity verification level required by the service. In addition, we propose an identity information sharing model that can provide the information to other service providers so that user can reuse verified identity information. Finally, we prove by implementing a system that verifies and manages only the identity assurance level required by the service through the enhanced user verification in the non-face-to-face identity proofing process.

• Journal of the Korea Convergence Society
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• v.11 no.1
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• pp.1-7
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• 2020

Recently 5G technology is one of the technologies that has been receiving much positive responses from users as it is integrated with virtual technology. However, 5G's security issues have not been fully resolved and more security is soon required. In this paper, an approach technique is proposed as a probability-based hierarchy to provide personal privacy for 5G-based IoT users more safely. The proposed technique is aimed at not exposing the privacy of IoT users to third parties by using two random keys created personally by IoT users. In order to satisfy both safety and efficiency, the proposed technology divides the privacy of IoT users into two layers. In the first stage, IoT users will control access to intermediate media using anonymous keys generated by IoT users, and in the second stage, information of IoT users registered with servers will be darkened and replicated. The proposed technique has improved the accuracy of the privacy protection of IoT users as they assign weights to layered information after layering users' privacy information on a probabilistic basis.

• Smart Media Journal
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• v.9 no.1
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• pp.60-66
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• 2020

In this paper, the FIDO (Fast IDentity Online) transaction certification platform was proposed for applying the DID (Decentralized ID) of blockchain with home shopping channels to the IPTV service providers based on the Remocon (Remote Control). In this case, the DID based smart remocon applies biometric identification techniques for personal identification. These individual DID smart remote controls apply distributed ID blockchain, enabling home shopping viewers to conduct reliable ratings surveys through the detection of channel changed information. In addition, this smart remocon utilizes the product purchased information history on home shopping channels, allowing IPTV's home shopping viewers to compare the same broadcasted production information on all channels by blockchain technique and their production characteristics. IPTV service providers can process home shopping order/authorization informations in one-stop service via a number of home shopping broadcasting companies, and DID smart remote controls for home shopping viewers with the checking results of their real-time online access to confirm the FIDO2.0 transaction certification homepage. Thus, the FIDO transaction authentication platforms of IPTV service provider(Telecommunication company) can be expected to improve the benefits of home shopping customers, and to reduce the broadcasting companies' burden of payment, too.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.192-200
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• 2008

Multimodal biometrics is a method for personal authentication and verification using more than two types of biometrics data. RBF based score-level fusion uses pattern recognition algorithm for multimodal biometrics, seeking the optimal decision boundary to classify score feature vectors each of which consists of matching scores obtained from several unimodal biometrics system for each sample. In this case, all matching scores are assumed to have the same reliability. However, in recent research it is reported that the quality of input sample affects the result of biometrics. Currently the matching scores having low reliability caused by low quality of samples are not currently considered for pattern recognition modelling in multimodal biometrics. To solve this problem, in this paper, we proposed the RBF based score-level fusion approach which employs quality information of input biometrics data to adjust decision boundary. As a result the proposed method with Qualify information showed better recognition performance than both the unimodal biometrics and the usual RBF based score-level fusion without using quality information.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.5
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• pp.93-106
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• 2006

With the rapid growth of the wireless Internet communication, a new generation of mobile devices have made possible the broad distribution of mobile digital contents, such as image, music, video, games and applications over the wireless Internet. Mobile devices are rapidly becoming the major means to extend communication channels without copy Protection, usage rule controlling and authentication. As a result, mobile digital contents may be illegally altered, copied and distributed among unauthorized mobile devices. In this paper, we take a look at Open Mobile Alliance (OMA) DRM v2.0 in general, its purpose and function. The OMA is uniquely the focal point for development of an open standard for mobile DRM. Next we introduces features for an active content and illustrates the difference between an active content and an inactive content. Enabling fast rendering of an active content, we propose an OMA-based DRM framework. This framework include the following: 1) Extending DCF Header for supporting an selective encryption, 2) Content encryption key management, 3) Rendering API for an active content. Experimental results show that the proposed framework is able to render an active content fast enough to satisfy Quality of Experience. %is framework has been proposed for a mobile device environment, but it is also applicable to other devices, such as portable media players, set-top boxes, or personal computer.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.31-40
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• 2023

Various policies and services are being implemented in Korea and other countries, such as the expansion of convenience facilities for mobility support, the provision of special means of transportation, and the establishment of public transportation route plans and fare policies based on data and AI-based movement pattern analysis to ensure the mobility rights of the weak in transportation. However, A research is still needed to improve service convenience in order to more conveniently use the desired means of transportation in a necessary situation from the viewpoint of the transportation vulnerable. This study examines the policies and services for the promotion of mobility for the transportation disadvantaged, and presents a MyData-based service model for mobility support for the transportation disadvantaged. In the proposed service model, the transportation-disabled person can freely choose and use the means of transportation according to individual circumstances, and receive the same transportation welfare voucher benefits provided by the state or government. The proposed service model defines the MyData platform that supports the safe collection and use of personal data, the authentication of traffic welfare recipients based on MyData, and the payment function for fee settlement after using the service as key components. In this research, the service satisfaction from the user's point of view was investigated by implementing the proposed service model and providing a demonstration service for the transportation vulnerable in Daejeon.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.97-117
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• 2012

Nowadays, Internet users tend to do a variety of actions at the same time such as web browsing, social networking and multimedia consumption. While watching a video, once a user is interested in any product, the user has to do information searches to get to know more about the product. With a conventional approach, user has to search it separately with search engines like Bing or Google, which might be inconvenient and time-consuming. For this reason, a video annotation platform has been developed in order to provide users more convenient and more interactive ways with video content. In the future of smart TV environment, users can follow annotated information, for example, a link to a vendor to buy the product of interest. It is even better to enable users to search for information by directly discussing with friends. Users can effectively get useful and relevant information about the product from friends who share common interests or might have experienced it before, which is more reliable than the results from search engines. Social networking services provide an appropriate environment for people to share products so that they can show new things to their friends and to share their personal experiences on any specific product. Meanwhile, they can also absorb the most relevant information about the product that they are interested in by either comments or discussion amongst friends. However, within a very huge graph of friends, determining the most appropriate persons to ask for information about a specific product has still a limitation within the existing conventional approach. Once users want to share or discuss a product, they simply share it to all friends as new feeds. This means a newly posted article is blindly spread to all friends without considering their background interests or knowledge. In this way, the number of responses back will be huge. Users cannot easily absorb the relevant and useful responses from friends, since they are from various fields of interest and knowledge. In order to overcome this limitation, we propose a method to filter a user's friends for information search, which leverages semantic video annotation and social networking services. Our method filters and brings out who can give user useful information about a specific product. By examining the existing Facebook information regarding users and their social graph, we construct a user profile of product interest. With user's permission and authentication, user's particular activities are enriched with the domain-specific ontology such as GoodRelations and BestBuy Data sources. Besides, we assume that the object in the video is already annotated using Linked Data. Thus, the detail information of the product that user would like to ask for more information is retrieved via product URI. Our system calculates the similarities among them in order to identify the most suitable friends for seeking information about the mentioned product. The system filters a user's friends according to their score which tells the order of whom can highly likely give the user useful information about a specific product of interest. We have conducted an experiment with a group of respondents in order to verify and evaluate our system. First, the user profile accuracy evaluation is conducted to demonstrate how much our system constructed user profile of product interest represents user's interest correctly. Then, the evaluation on filtering method is made by inspecting the ranked results with human judgment. The results show that our method works effectively and efficiently in filtering. Our system fulfills user needs by supporting user to select appropriate friends for seeking useful information about a specific product that user is curious about. As a result, it helps to influence and convince user in purchase decisions.