CompNet: Competitive Neural Network for Palmprint Recognition Using Learnable Gabor Kernels
Contactless palmprint recognition has recently made significant progress in palm-scanning payment and social security. However, most existing methods are based on handcrafted kernels and are sensitive to illumination and scale variations. To address this problem, a competitive convolutional neural n...
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| Veröffentlicht in: | IEEE signal processing letters 2021, Vol.28, p.1739-1743 |
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| creator | Liang, Xu Yang, Jinyang Lu, Guangming Zhang, David |
| description | Contactless palmprint recognition has recently made significant progress in palm-scanning payment and social security. However, most existing methods are based on handcrafted kernels and are sensitive to illumination and scale variations. To address this problem, a competitive convolutional neural network (CompNet) with constrained learnable Gabor filters is proposed for contactless palmprint recognition. The proposed CompNet is built on multisize competitive blocks, which are applied to effectively exploit the rich direction ordering information of the palmprint patterns by means of the ad-hoc softmax and channel-wise convolution operations. Compared to the current deep neural networks, the backbone of the proposed network contains only very few parameters, making it quite easy to train, especially on small-scale datasets. Experimental results obtained on four popular contactless palmprint datasets demonstrate that the proposed CompNet achieves the lowest equal error rate compared to the most commonly used methods. |
| doi_str_mv | 10.1109/LSP.2021.3103475 |
| format | Article |
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However, most existing methods are based on handcrafted kernels and are sensitive to illumination and scale variations. To address this problem, a competitive convolutional neural network (CompNet) with constrained learnable Gabor filters is proposed for contactless palmprint recognition. The proposed CompNet is built on multisize competitive blocks, which are applied to effectively exploit the rich direction ordering information of the palmprint patterns by means of the ad-hoc softmax and channel-wise convolution operations. Compared to the current deep neural networks, the backbone of the proposed network contains only very few parameters, making it quite easy to train, especially on small-scale datasets. Experimental results obtained on four popular contactless palmprint datasets demonstrate that the proposed CompNet achieves the lowest equal error rate compared to the most commonly used methods.</description><identifier>ISSN: 1070-9908</identifier><identifier>EISSN: 1558-2361</identifier><identifier>DOI: 10.1109/LSP.2021.3103475</identifier><identifier>CODEN: ISPLEM</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Artificial neural networks ; Biometrics ; competitive block ; competitive feature encoder ; Convolution ; Data mining ; Datasets ; Feature extraction ; Gabor filters ; Kernel ; Kernels ; learnable Gabor filter ; Neural networks ; Palmprint recognition ; Password ; Recognition ; Shape ; Social security</subject><ispartof>IEEE signal processing letters, 2021, Vol.28, p.1739-1743</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c357t-a4319e63883cbc9b33ae85f1b8d0d8a88e586f41df30fa9b51d7c925ee1633e03</citedby><cites>FETCH-LOGICAL-c357t-a4319e63883cbc9b33ae85f1b8d0d8a88e586f41df30fa9b51d7c925ee1633e03</cites><orcidid>0000-0003-1578-2634 ; 0000-0003-4332-3494 ; 0000-0002-5027-5286</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9512475$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,4010,27900,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9512475$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Liang, Xu</creatorcontrib><creatorcontrib>Yang, Jinyang</creatorcontrib><creatorcontrib>Lu, Guangming</creatorcontrib><creatorcontrib>Zhang, David</creatorcontrib><title>CompNet: Competitive Neural Network for Palmprint Recognition Using Learnable Gabor Kernels</title><title>IEEE signal processing letters</title><addtitle>LSP</addtitle><description>Contactless palmprint recognition has recently made significant progress in palm-scanning payment and social security. However, most existing methods are based on handcrafted kernels and are sensitive to illumination and scale variations. To address this problem, a competitive convolutional neural network (CompNet) with constrained learnable Gabor filters is proposed for contactless palmprint recognition. The proposed CompNet is built on multisize competitive blocks, which are applied to effectively exploit the rich direction ordering information of the palmprint patterns by means of the ad-hoc softmax and channel-wise convolution operations. Compared to the current deep neural networks, the backbone of the proposed network contains only very few parameters, making it quite easy to train, especially on small-scale datasets. Experimental results obtained on four popular contactless palmprint datasets demonstrate that the proposed CompNet achieves the lowest equal error rate compared to the most commonly used methods.</description><subject>Artificial neural networks</subject><subject>Biometrics</subject><subject>competitive block</subject><subject>competitive feature encoder</subject><subject>Convolution</subject><subject>Data mining</subject><subject>Datasets</subject><subject>Feature extraction</subject><subject>Gabor filters</subject><subject>Kernel</subject><subject>Kernels</subject><subject>learnable Gabor filter</subject><subject>Neural networks</subject><subject>Palmprint recognition</subject><subject>Password</subject><subject>Recognition</subject><subject>Shape</subject><subject>Social security</subject><issn>1070-9908</issn><issn>1558-2361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMFLwzAUxoMoOKd3wUvAc2de07SJNxk6xTKHupOHkLavo7NrZtIp_vembHj6Pni_7_HeR8glsAkAUzf522ISsxgmHBhPMnFERiCEjGKewnHwLGORUkyekjPv14wxCVKMyMfUbrZz7G_pYLBv-uYb6Rx3zrRB-h_rPmltHV2YdrN1TdfTVyztqgug7ejSN92K5mhcZ4oW6cwUgX1G12Hrz8lJbVqPFwcdk-XD_fv0McpfZk_Tuzwqucj6yCQcFKZcSl4WpSo4NyhFDYWsWCWNlChkWidQ1ZzVRhUCqqxUsUCElHNkfEyu93u3zn7t0Pd6bXfhoNbrWGRxmiUKBortqdJZ7x3WOryzMe5XA9NDhTpUqIcK9aHCELnaRxpE_MeVgHiY_gHXRW0b</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Liang, Xu</creator><creator>Yang, Jinyang</creator><creator>Lu, Guangming</creator><creator>Zhang, David</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0003-1578-2634</orcidid><orcidid>https://orcid.org/0000-0003-4332-3494</orcidid><orcidid>https://orcid.org/0000-0002-5027-5286</orcidid></search><sort><creationdate>2021</creationdate><title>CompNet: Competitive Neural Network for Palmprint Recognition Using Learnable Gabor Kernels</title><author>Liang, Xu ; Yang, Jinyang ; Lu, Guangming ; Zhang, David</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c357t-a4319e63883cbc9b33ae85f1b8d0d8a88e586f41df30fa9b51d7c925ee1633e03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial neural networks</topic><topic>Biometrics</topic><topic>competitive block</topic><topic>competitive feature encoder</topic><topic>Convolution</topic><topic>Data mining</topic><topic>Datasets</topic><topic>Feature extraction</topic><topic>Gabor filters</topic><topic>Kernel</topic><topic>Kernels</topic><topic>learnable Gabor filter</topic><topic>Neural networks</topic><topic>Palmprint recognition</topic><topic>Password</topic><topic>Recognition</topic><topic>Shape</topic><topic>Social security</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liang, Xu</creatorcontrib><creatorcontrib>Yang, Jinyang</creatorcontrib><creatorcontrib>Lu, Guangming</creatorcontrib><creatorcontrib>Zhang, David</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE signal processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Liang, Xu</au><au>Yang, Jinyang</au><au>Lu, Guangming</au><au>Zhang, David</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>CompNet: Competitive Neural Network for Palmprint Recognition Using Learnable Gabor Kernels</atitle><jtitle>IEEE signal processing letters</jtitle><stitle>LSP</stitle><date>2021</date><risdate>2021</risdate><volume>28</volume><spage>1739</spage><epage>1743</epage><pages>1739-1743</pages><issn>1070-9908</issn><eissn>1558-2361</eissn><coden>ISPLEM</coden><abstract>Contactless palmprint recognition has recently made significant progress in palm-scanning payment and social security. However, most existing methods are based on handcrafted kernels and are sensitive to illumination and scale variations. To address this problem, a competitive convolutional neural network (CompNet) with constrained learnable Gabor filters is proposed for contactless palmprint recognition. The proposed CompNet is built on multisize competitive blocks, which are applied to effectively exploit the rich direction ordering information of the palmprint patterns by means of the ad-hoc softmax and channel-wise convolution operations. Compared to the current deep neural networks, the backbone of the proposed network contains only very few parameters, making it quite easy to train, especially on small-scale datasets. Experimental results obtained on four popular contactless palmprint datasets demonstrate that the proposed CompNet achieves the lowest equal error rate compared to the most commonly used methods.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/LSP.2021.3103475</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0003-1578-2634</orcidid><orcidid>https://orcid.org/0000-0003-4332-3494</orcidid><orcidid>https://orcid.org/0000-0002-5027-5286</orcidid></addata></record> |
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| subjects | Artificial neural networks Biometrics competitive block competitive feature encoder Convolution Data mining Datasets Feature extraction Gabor filters Kernel Kernels learnable Gabor filter Neural networks Palmprint recognition Password Recognition Shape Social security |
| title | CompNet: Competitive Neural Network for Palmprint Recognition Using Learnable Gabor Kernels |
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