Frames extracted from video streaming to recognition of face: LBPH, FF and CNN
There are different methods that can be used to uniquely identify one individual from another like Face recognition, Fingerprints, Iris, Signature, etc. Face recognition has gained a lot of popularity due to its wide range of applications in image processing, biometrics. The objective of this projec...
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| creator | Shankar, R. Shiva Raminaidu, Ch Rajanikanth, J. Raghaveni, J. |
| description | There are different methods that can be used to uniquely identify one individual from another like Face recognition, Fingerprints, Iris, Signature, etc. Face recognition has gained a lot of popularity due to its wide range of applications in image processing, biometrics. The objective of this project is to detect a human face and identify the person from the set of images from the database when the camera is streaming. A database is created by capturing frames by the camera. The LBPH algorithm, Fisher Face algorithms are used for Face Detection, Feature Extraction and Face Recognition. A Convolutional Neural Network with different layers is constructed, and the model is compiled. The result shows recognized faces when camera is streaming. |
| doi_str_mv | 10.1063/5.0178700 |
| format | Conference Proceeding |
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Shiva ; Raminaidu, Ch ; Rajanikanth, J. ; Raghaveni, J.</creator><contributor>P, Thangaraj ; H, Shankar ; K, Mohana Sundaram</contributor><creatorcontrib>Shankar, R. Shiva ; Raminaidu, Ch ; Rajanikanth, J. ; Raghaveni, J. ; P, Thangaraj ; H, Shankar ; K, Mohana Sundaram</creatorcontrib><description>There are different methods that can be used to uniquely identify one individual from another like Face recognition, Fingerprints, Iris, Signature, etc. Face recognition has gained a lot of popularity due to its wide range of applications in image processing, biometrics. The objective of this project is to detect a human face and identify the person from the set of images from the database when the camera is streaming. A database is created by capturing frames by the camera. The LBPH algorithm, Fisher Face algorithms are used for Face Detection, Feature Extraction and Face Recognition. A Convolutional Neural Network with different layers is constructed, and the model is compiled. The result shows recognized faces when camera is streaming.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0178700</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Algorithms ; Artificial neural networks ; Biometric recognition systems ; Cameras ; Face recognition ; Feature extraction ; Frames (data processing) ; Image processing ; Video transmission</subject><ispartof>AIP conference proceedings, 2023, Vol.2901 (1)</ispartof><rights>Author(s)</rights><rights>2023 Author(s). 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Shiva</creatorcontrib><creatorcontrib>Raminaidu, Ch</creatorcontrib><creatorcontrib>Rajanikanth, J.</creatorcontrib><creatorcontrib>Raghaveni, J.</creatorcontrib><title>Frames extracted from video streaming to recognition of face: LBPH, FF and CNN</title><title>AIP conference proceedings</title><description>There are different methods that can be used to uniquely identify one individual from another like Face recognition, Fingerprints, Iris, Signature, etc. Face recognition has gained a lot of popularity due to its wide range of applications in image processing, biometrics. The objective of this project is to detect a human face and identify the person from the set of images from the database when the camera is streaming. A database is created by capturing frames by the camera. The LBPH algorithm, Fisher Face algorithms are used for Face Detection, Feature Extraction and Face Recognition. A Convolutional Neural Network with different layers is constructed, and the model is compiled. The result shows recognized faces when camera is streaming.</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Biometric recognition systems</subject><subject>Cameras</subject><subject>Face recognition</subject><subject>Feature extraction</subject><subject>Frames (data processing)</subject><subject>Image processing</subject><subject>Video transmission</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2023</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNotkM1KAzEYRYMoWKsL3yDgTpz6JZn8jDstjhVKdaHgLmQySZniTGqSFn17W9rV3RzuuVyErglMCAh2zydApJIAJ2hEOCeFFEScohFAVRa0ZF_n6CKlFQCtpFQjtKij6V3C7jdHY7NrsY-hx9uudQGnHJ3pu2GJc8DR2bAcutyFAQePvbHuAc-f3md3uK6xGVo8XSwu0Zk338ldHXOMPuvnj-msmL-9vE4f58WaMJYLDo5UvnJGOVkpZSQ1oFpBaCOs54Jw2ziqJG2tKJlojHQlb0oKYLhvuZRsjG4OvesYfjYuZb0KmzjslJpWQHcSIciOuj1QyXbZ7Jfrdex6E_80Ab3_S3N9_Iv9AwNRWlc</recordid><startdate>20231215</startdate><enddate>20231215</enddate><creator>Shankar, R. 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Shiva ; Raminaidu, Ch ; Rajanikanth, J. ; Raghaveni, J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p133t-50e19f9ea8e7988a72a08d612b6cf5615cbe2872dc6436ba7e45b4200a5fd5773</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Biometric recognition systems</topic><topic>Cameras</topic><topic>Face recognition</topic><topic>Feature extraction</topic><topic>Frames (data processing)</topic><topic>Image processing</topic><topic>Video transmission</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shankar, R. Shiva</creatorcontrib><creatorcontrib>Raminaidu, Ch</creatorcontrib><creatorcontrib>Rajanikanth, J.</creatorcontrib><creatorcontrib>Raghaveni, J.</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shankar, R. Shiva</au><au>Raminaidu, Ch</au><au>Rajanikanth, J.</au><au>Raghaveni, J.</au><au>P, Thangaraj</au><au>H, Shankar</au><au>K, Mohana Sundaram</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Frames extracted from video streaming to recognition of face: LBPH, FF and CNN</atitle><btitle>AIP conference proceedings</btitle><date>2023-12-15</date><risdate>2023</risdate><volume>2901</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>There are different methods that can be used to uniquely identify one individual from another like Face recognition, Fingerprints, Iris, Signature, etc. Face recognition has gained a lot of popularity due to its wide range of applications in image processing, biometrics. The objective of this project is to detect a human face and identify the person from the set of images from the database when the camera is streaming. A database is created by capturing frames by the camera. 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| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP conference proceedings, 2023, Vol.2901 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
| recordid | cdi_proquest_journals_2902133661 |
| source | AIP Journals Complete |
| subjects | Algorithms Artificial neural networks Biometric recognition systems Cameras Face recognition Feature extraction Frames (data processing) Image processing Video transmission |
| title | Frames extracted from video streaming to recognition of face: LBPH, FF and CNN |
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