Development of a personal identification technique for automation systems
The given article considers the development of a personal identification technique based on the mechanism of scanning and analyzing such biometric parameter as a vein pattern of the palm for automation access control systems. A number of problems characteristic of the existing approaches to solving...
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| Veröffentlicht in: | IOP conference series. Materials Science and Engineering 2021-02, Vol.1047 (1), p.12138 |
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| creator | Chastikova, V A Zherlitsyn, S A Volya, Y I |
| description | The given article considers the development of a personal identification technique based on the mechanism of scanning and analyzing such biometric parameter as a vein pattern of the palm for automation access control systems. A number of problems characteristic of the existing approaches to solving the given problem have been formulated and the operation analysis of the main ones has been carried out. A mechanism for reading a vein pattern of the palm, as well as three methods for further analysis of the referred biometrics and personal identification: a method based on a categorical classification, a method based on a binary classification, and a combined method have been developed. The resulting architecture of the neural network for the categorical classification of the vein pattern has been built and a method for calculating the number of the model parameters depending on the number of the registered subjects has been obtained. Based on the results of the research, experimental measurements of the system operation accuracy have been made while implementing the mentioned methods. The system based on a binary classification has demonstrated the highest accuracy; however applying a combined approach allows improving the obtained result. |
| doi_str_mv | 10.1088/1757-899X/1047/1/012138 |
| format | Article |
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A number of problems characteristic of the existing approaches to solving the given problem have been formulated and the operation analysis of the main ones has been carried out. A mechanism for reading a vein pattern of the palm, as well as three methods for further analysis of the referred biometrics and personal identification: a method based on a categorical classification, a method based on a binary classification, and a combined method have been developed. The resulting architecture of the neural network for the categorical classification of the vein pattern has been built and a method for calculating the number of the model parameters depending on the number of the registered subjects has been obtained. Based on the results of the research, experimental measurements of the system operation accuracy have been made while implementing the mentioned methods. 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The resulting architecture of the neural network for the categorical classification of the vein pattern has been built and a method for calculating the number of the model parameters depending on the number of the registered subjects has been obtained. Based on the results of the research, experimental measurements of the system operation accuracy have been made while implementing the mentioned methods. The system based on a binary classification has demonstrated the highest accuracy; however applying a combined approach allows improving the obtained result.</description><subject>Access control</subject><subject>Automation</subject><subject>Biometrics</subject><subject>Classification</subject><subject>Neural networks</subject><subject>Parameters</subject><issn>1757-8981</issn><issn>1757-899X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqFkE1LwzAYgIMoOKe_wYAnD7VJmiXpUeamg4kHFbyFNh-YsTY16YT9e1MqE0HwlJe8z_v1AHCJ0Q1GQuSYz3gmyvItx4jyHOcIE1yIIzA5ZI4PscCn4CzGDUKMU4omYHVnPs3Wd41pe-gtrGBnQvRttYVOpz9nnap651vYG_Xeuo-dgdYHWO1634yJuI-9aeI5OLHVNpqL73cKXpeLl_lDtn66X81v15kqCBdZyZG2ShGtNVWm4oYzRUzBlBaUM1qXgiNCmWY15UqxWhNRW4vSUaa0muNiCq7Gvl3waZvYy43fhbRwlGSGC8QQKcpE8ZFSwccYjJVdcE0V9hIjOXiTgxE52JGDN4nl6C1VXo-Vznc_rR-fF7852Wmb2OIP9r8JX5J-fnc</recordid><startdate>20210201</startdate><enddate>20210201</enddate><creator>Chastikova, V A</creator><creator>Zherlitsyn, S A</creator><creator>Volya, Y I</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>KB.</scope><scope>L6V</scope><scope>M7S</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20210201</creationdate><title>Development of a personal identification technique for automation systems</title><author>Chastikova, V A ; Zherlitsyn, S A ; Volya, Y I</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3278-970dfcc2ddd4cea7e76c2e36cd84764b9870246d6b47cc6bd28bff0012e9fd713</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Access control</topic><topic>Automation</topic><topic>Biometrics</topic><topic>Classification</topic><topic>Neural networks</topic><topic>Parameters</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chastikova, V A</creatorcontrib><creatorcontrib>Zherlitsyn, S A</creatorcontrib><creatorcontrib>Volya, Y I</creatorcontrib><collection>IOP Publishing Free Content</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Materials Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>IOP conference series. 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A number of problems characteristic of the existing approaches to solving the given problem have been formulated and the operation analysis of the main ones has been carried out. A mechanism for reading a vein pattern of the palm, as well as three methods for further analysis of the referred biometrics and personal identification: a method based on a categorical classification, a method based on a binary classification, and a combined method have been developed. The resulting architecture of the neural network for the categorical classification of the vein pattern has been built and a method for calculating the number of the model parameters depending on the number of the registered subjects has been obtained. Based on the results of the research, experimental measurements of the system operation accuracy have been made while implementing the mentioned methods. 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| source | IOP Publishing Free Content; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; IOPscience extra; Free Full-Text Journals in Chemistry |
| subjects | Access control Automation Biometrics Classification Neural networks Parameters |
| title | Development of a personal identification technique for automation systems |
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