• Journal of Digital Convergence
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• v.18 no.7
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• pp.123-133
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• 2020

The aim of this study is to scrutinize acceptance intentions of Korean users and influences of information security related factors on mobile payment services based on biometric authentication methods, like finger print authentication or face recognition authentication, by focusing on ApplePay. Unlike previous studies on user acceptance of mobile payment which lack considerations on information security related factors, this study employs the UTAUT with detailed information security factors to create a research model and PLS(Partial Least Squares) method to analyze the model. Based on the analysis, gaining trust on service through company's efforts on information protection, personal characteristics and trust on applied security technologies are important factors to Korean users along with social awareness and service infrastructures. The result of this study would be helpful to companies or organizations, which provide biometric-based mobile payment services, to understand needs of Korean consumers. Based on this study, further analysis is expected to find impacts of user experiences on same company's or competitors' products to acceptance intentions.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.17-23
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• 2020

Biometric information indicating measurement items related to human characteristics has attracted great attention as security technology with high reliability since there is no fear of theft or loss. Among these biometric information, fingerprints are mainly used in fields such as identity verification and identification. If there is a problem such as a wound, wrinkle, or moisture that is difficult to authenticate to the fingerprint image when identifying the identity, the fingerprint expert can identify the problem with the fingerprint directly through the preprocessing step, and apply the image processing algorithm appropriate to the problem. Solve the problem. In this case, by implementing artificial intelligence software that distinguishes fingerprint images with cuts and wrinkles on the fingerprint, it is easy to check whether there are cuts or wrinkles, and by selecting an appropriate algorithm, the fingerprint image can be easily improved. In this study, we developed a total of 17,080 fingerprint databases by acquiring all finger prints of 1,010 students from the Royal University of Cambodia, 600 Sokoto open data sets, and 98 Korean students. In order to determine if there are any injuries or wrinkles in the built database, criteria were established, and the data were validated by experts. The training and test datasets consisted of Cambodian data and Sokoto data, and the ratio was set to 8: 2. The data of 98 Korean students were set up as a validation data set. Using the constructed data set, five CNN-based architectures such as Classic CNN, AlexNet, VGG-16, Resnet50, and Yolo v3 were implemented. A study was conducted to find the model that performed best on the readings. Among the five architectures, ResNet50 showed the best performance with 81.51%.