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公开(公告)号：US11449587B2
公开(公告)日：2022-09-20
申请号：US16194867
申请日：2018-11-19
申请人： PPIP LLC
发明人： Michael Fong , Neric Hsin-wu Fong , Stephen Kuo-Tung Seay Chan , Krishna Teja Tokola , Huyen Nguyen Ngoc Cam Le
IPC分类号： G06F21/32 , H04L29/06 , G06N3/08 , G06K9/62 , H04L9/40 , G06V10/40 , G06V20/80 , G06V40/70 , G06V40/16 , G06N20/00 , H04W12/06 , G06V10/94 , G06V40/12
摘要： In accordance with some embodiments, an apparatus for privacy protection is provided. The apparatus includes a housing arranged to hold a second device; one or more sensors, at least partially supported by the housing, operable to continuously collect biometric data of a user; an authentication neural network, operable to extract from the biometric data a plurality feature vectors associated with a plurality of identifiable scores; and a decision unit, coupled to the authentication neural network, operable to generate an authentication score as a function of the plurality of identifiability scores and the plurality of feature vectors, determine whether or not the authentication score satisfies an authentication threshold, and gate electronic access to the second device base on whether or not the authentication score satisfies the authentication threshold.
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公开(公告)号：US20190354787A1
公开(公告)日：2019-11-21
申请号：US16194809
申请日：2018-11-19
申请人： PPIP LLC
发明人： Michael Fong , Neric Hsin-wu Fong , Stephen Kuo-Tung Seay Chan , Krishna Teja Tokola , Huyen Nguyen Ngoc Cam Le
摘要： In accordance with some embodiments, a training method for biometric identity and authentication is provided. The method includes obtaining biometric data from a plurality of sources. The method further includes extracting a plurality of feature vectors from the biometric data. The method also includes determining a plurality of identifiability scores correspondingly associated with the plurality of feature vectors, where each of the plurality of identifiability scores provides a quantitative characterization of a relative uniqueness of a corresponding one of the plurality of feature vectors. The method additional includes determining run-time authentication neural network parameters based on a function of the plurality of feature vectors, where the run-time authentication neural network parameters enable extraction of one or more feature vectors from biometric data of a particular user, and the run-time authentication neural network parameters are associated with the plurality of feature vectors determined to satisfy an error threshold.
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公开(公告)号：US11132427B2
公开(公告)日：2021-09-28
申请号：US16194815
申请日：2018-11-19
申请人： PPIP LLC
发明人： Michael Fong , Neric Hsin-Wu Fong , Stephen Kuo-Tung Seay Chan , Krishna Teja Tokola , Huyen Nguyen Ngoc Cam Le
摘要： In accordance with some embodiments, a training method for biometric identity and authentication is provided. The method includes obtaining biometric data from a plurality of sources. The method further includes establishing a candidate set of neural network parameters. The method additional includes extracting a plurality of feature vectors from the biometric data using the candidate set of neural network parameters. The method also includes determining whether or not the plurality of feature vectors match a training vector set within an error threshold. The method further includes updating the candidate set of neural network parameters in response to determining that the plurality of feature vectors do not match the training vector set within the error threshold.
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公开(公告)号：US20190354660A1
公开(公告)日：2019-11-21
申请号：US16194815
申请日：2018-11-19
申请人： PPIP LLC
发明人： Michael Fong , Neric Hsin-wu Fong , Stephen Kuo-Tung Seay Chan , Krishna Teja Tokola , Huyen Nguyen Ngoc Cam Le
摘要： In accordance with some embodiments, a training method for biometric identity and authentication is provided. The method includes obtaining biometric data from a plurality of sources. The method further includes establishing a candidate set of neural network parameters. The method additional includes extracting a plurality of feature vectors from the biometric data using the candidate set of neural network parameters. The method also includes determining whether or not the plurality of feature vectors match a training vector set within an error threshold. The method further includes updating the candidate set of neural network parameters in response to determining that the plurality of feature vectors do not match the training vector set within the error threshold.
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