Person Identification Based on Accelerations Sensed in Smartphones with LSTM (Preprint)
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説明
User identification is an important task for a variety of purposes such as authentication or providing personalized advice for improving user experience. In this paper, we propose a method for identifying a user who is holding a smartphone out of previously given target users from acceleration data obtained from the accelerometer in the smartphone using a deep neural network. This proposed method preliminary creates the model from the acceleration data of each user while walking in its training phase. This method identifies the user from acceleration data for identification based on this model in the classification phase. We evaluated the proposed method with the acceleration obtained from the actual eight and twelve users in two aspects, which were identifications including no-decision choice and that without no-decision choice. Our evaluation showed that the proposed method achieved accuracies higher than 95% for two- to five-class identification without no-decision. The proposed method identified the user with no or little false positive in evaluations with “no-decision.” ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.29(2021) (online) ------------------------------
User identification is an important task for a variety of purposes such as authentication or providing personalized advice for improving user experience. In this paper, we propose a method for identifying a user who is holding a smartphone out of previously given target users from acceleration data obtained from the accelerometer in the smartphone using a deep neural network. This proposed method preliminary creates the model from the acceleration data of each user while walking in its training phase. This method identifies the user from acceleration data for identification based on this model in the classification phase. We evaluated the proposed method with the acceleration obtained from the actual eight and twelve users in two aspects, which were identifications including no-decision choice and that without no-decision choice. Our evaluation showed that the proposed method achieved accuracies higher than 95% for two- to five-class identification without no-decision. The proposed method identified the user with no or little false positive in evaluations with “no-decision.” ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.29(2021) (online) ------------------------------
収録刊行物
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- 情報処理学会論文誌コンシューマ・デバイス&システム(CDS)
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情報処理学会論文誌コンシューマ・デバイス&システム(CDS) 11 (3), 2021-09-30
情報処理学会
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詳細情報 詳細情報について
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- CRID
- 1050852581577699840
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- NII論文ID
- 170000185574
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- NII書誌ID
- AA12628043
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- ISSN
- 21865728
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- 本文言語コード
- en
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- 資料種別
- journal article
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- データソース種別
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- IRDB
- CiNii Articles