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ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2021,3,19]]},"abstract":"<jats:p>Person identification plays a critical role in a large range of applications. Recently, RF based person identification becomes a hot research topic due to the contact-free nature of RF sensing that is particularly appealing in current COVID-19 pandemic. However, existing systems still have multiple limitations: i) heavily rely on the gait patterns of users for identification; ii) require a large amount of data to train the model and also extensive retraining for new users and iii) require a large frequency bandwidth which is not available on most commodity RF devices for static person identification. This paper proposes RF-Identity, an RFID-based identification system to address the above limitations and the contribution is threefold. First, by integrating walking pattern features with unique body shape features (e.g., height), RF-Identity achieves a high accuracy in person identification. Second, RF-Identity develops a data augmentation scheme to expand the size of the training data set, thus reducing the human effort in data collection. Third, RF-Identity utilizes the tag diversity in spatial domain to identify static users without a need of large frequency bandwidth. Extensive experiments show an identification accuracy of 94.2% and 95.9% for 50 dynamic and static users, respectively.<\/jats:p>","DOI":"10.1145\/3448101","type":"journal-article","created":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T18:56:41Z","timestamp":1617130601000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":43,"title":["RF-Identity"],"prefix":"10.1145","volume":"5","author":[{"given":"Chao","family":"Feng","sequence":"first","affiliation":[{"name":"Northwest University, IoT Research Center-Northwest University, China"}]},{"given":"Jie","family":"Xiong","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, USA"}]},{"given":"Liqiong","family":"Chang","sequence":"additional","affiliation":[{"name":"Northwest University, International Joint Research Centre for Battery-free IoT, China"}]},{"given":"Fuwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Northwest University, Northwest University-Jingdong Wisdom Cloud Joint Research Center for AI &amp; IoT, China"}]},{"given":"Ju","family":"Wang","sequence":"additional","affiliation":[{"name":"IoT Research Center- Northwest University, China"}]},{"given":"Dingyi","family":"Fang","sequence":"additional","affiliation":[{"name":"Northwest University, IoT Research Center-Northwest University, International Joint Research Centre for Battery-free IoT, China"}]}],"member":"320","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"[n. d.]. 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