| Title: | A biometric authentication model using hand gesture images |
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| Authors: | ID Fong, Simon (Author)
ID Zhuang, Yan (Author)
ID Fister, Iztok (Author)
ID Fister, Iztok (Author) |
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| Files: |  BioMedical_Engineering_OnLine_2013_Fong_et_al._A_biometric_authentication_model_using_hand_gesture_images.pdf (1,83 MB)
MD5: 5D182D45E604C1E634BCD7FFE341D76F
 http://biomedical-engineering-online.biomedcentral.com/articles/10.1186/1475-925X-12-111
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | A novel hand biometric authentication method based on measurements of the user's stationary hand gesture of hand sign language is proposed. The measurement of hand gestures could be sequentially acquired by a low-cost video camera. There could possibly be another level of contextual information,associated with these hand signs to be used in biometric authentication. As an analogue, instead of typing a password 'iloveu' in text which is relatively vulnerable over a communication network, a signer can encode a biometric password using a sequence of hand signs, 'i', 'l', 'o', 'v', 'e', and 'u'. Subsequently the features from the hand gesture images are extracted which are integrally fuzzy in nature, to be recognized by a classification model for telling if this signer is who he claimed himself to be, by examining over his hand shape and the postures in doing those signs. Itis believed that everybody has certain slight but unique behavioral characteristics in sign language, so are the different hand shape compositions. Simple and efficient image processing algorithms are used in hand sign recognition, including intensity profiling, color histogram and dimensionality analysis, coupled with several popular machine learning algorithms. Computer simulation is conducted for investigating the efficacy ofthis novel biometric authentication model which shows up to 93.75% recognition accuracy. |
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| Keywords: | biometric authentication, hand gesture, hand sign recognition, machine learning |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Year of publishing: | 2013 |
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| Number of pages: | str. 1-26 |
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| Numbering: | Letn. 12 |
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| PID: | 20.500.12556/DKUM-66482  |
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| ISSN: | 1475-925X |
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| UDC: | 004.8 |
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| ISSN on article: | 1475-925X |
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| COBISS.SI-ID: | 17279254 |
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| DOI: | 10.1186/1475-925X-12-111 |
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| NUK URN: | URN:SI:UM:DK:GACTATZV |
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| Publication date in DKUM: | 28.06.2017 |
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| Views: | 2116 |
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| Downloads: | 483 |
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| Metadata: |    |
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| Categories: | Misc.
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