Conference
Police sketch generator using generative adversarial networks.
| Τίτλος: | Police sketch generator using generative adversarial networks. |
|---|---|
| Συγγραφείς: | Poornima, Ediga, Tarang, Goggi Ruthvik, Numaan, Syed Khasim, Kashyap, Burra, Seshappa, Angadi, Ihassan, Mohammed, Saini, Manisha, Sehgal, Archana |
| Πηγή: | AIP Conference Proceedings; 2025, Vol. 3263 Issue 1, p1-16, 16p |
| Θεματικοί όροι: | Generative adversarial networks, Criminal investigation, Program generators (Computer programs), Pen drawing, Eyewitness accounts, Police, Artificial intelligence, Face perception |
| Περίληψη: | Facial sketches drawn by a traditional artist have long occupied an exclusive place in the field of law enforcement as a quick-communicator for what somebody looks like based on eyewitness accounts when pursuing fugitives. Other studies have attempted to improve this method and compared it with all points bulletins 222 or composites. However, the creation of facial composites via applications has always been a time-consuming endeavor. This work showcases a novel use of GANs to devise an application that can morph police sketches in new way. The main purpose of this unbelievable tool, is to produce nearly natural however completely imaginary human faces with changeable sliders such as skin color, facial hair and head form. It quickly generates thousands of unique facial profiles that resemble real people. Using input from eyewitness descriptions, the generator network then improves that output accordingly to better fit with what was described verbally. As the GAN continues to train, it learns how to create detailed sketches with fine distinctions in facial features, expressions or other details. This is a major benefit for criminal investigations by speeding the process of creating an accurate suspect sketch, which could be key in identifying and catching someone wanted. [ABSTRACT FROM AUTHOR] |
| Copyright of AIP Conference Proceedings is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 187395242 RelevancyScore: 1009 AccessLevel: 6 PubType: Conference PubTypeId: conference PreciseRelevancyScore: 1008.54614257813 |
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| Items | – Name: Title Label: Title Group: Ti Data: Police sketch generator using generative adversarial networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Poornima%2C+Ediga%22">Poornima, Ediga</searchLink><br /><searchLink fieldCode="AR" term="%22Tarang%2C+Goggi+Ruthvik%22">Tarang, Goggi Ruthvik</searchLink><br /><searchLink fieldCode="AR" term="%22Numaan%2C+Syed+Khasim%22">Numaan, Syed Khasim</searchLink><br /><searchLink fieldCode="AR" term="%22Kashyap%2C+Burra%22">Kashyap, Burra</searchLink><br /><searchLink fieldCode="AR" term="%22Seshappa%2C+Angadi%22">Seshappa, Angadi</searchLink><br /><searchLink fieldCode="AR" term="%22Ihassan%2C+Mohammed%22">Ihassan, Mohammed</searchLink><br /><searchLink fieldCode="AR" term="%22Saini%2C+Manisha%22">Saini, Manisha</searchLink><br /><searchLink fieldCode="AR" term="%22Sehgal%2C+Archana%22">Sehgal, Archana</searchLink> – Name: TitleSource Label: Source Group: Src Data: AIP Conference Proceedings; 2025, Vol. 3263 Issue 1, p1-16, 16p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Generative+adversarial+networks%22">Generative adversarial networks</searchLink><br /><searchLink fieldCode="DE" term="%22Criminal+investigation%22">Criminal investigation</searchLink><br /><searchLink fieldCode="DE" term="%22Program+generators+%28Computer+programs%29%22">Program generators (Computer programs)</searchLink><br /><searchLink fieldCode="DE" term="%22Pen+drawing%22">Pen drawing</searchLink><br /><searchLink fieldCode="DE" term="%22Eyewitness+accounts%22">Eyewitness accounts</searchLink><br /><searchLink fieldCode="DE" term="%22Police%22">Police</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Face+perception%22">Face perception</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Facial sketches drawn by a traditional artist have long occupied an exclusive place in the field of law enforcement as a quick-communicator for what somebody looks like based on eyewitness accounts when pursuing fugitives. Other studies have attempted to improve this method and compared it with all points bulletins 222 or composites. However, the creation of facial composites via applications has always been a time-consuming endeavor. This work showcases a novel use of GANs to devise an application that can morph police sketches in new way. The main purpose of this unbelievable tool, is to produce nearly natural however completely imaginary human faces with changeable sliders such as skin color, facial hair and head form. It quickly generates thousands of unique facial profiles that resemble real people. Using input from eyewitness descriptions, the generator network then improves that output accordingly to better fit with what was described verbally. As the GAN continues to train, it learns how to create detailed sketches with fine distinctions in facial features, expressions or other details. This is a major benefit for criminal investigations by speeding the process of creating an accurate suspect sketch, which could be key in identifying and catching someone wanted. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of AIP Conference Proceedings is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1063/5.0261372 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1 Subjects: – SubjectFull: Generative adversarial networks Type: general – SubjectFull: Criminal investigation Type: general – SubjectFull: Program generators (Computer programs) Type: general – SubjectFull: Pen drawing Type: general – SubjectFull: Eyewitness accounts Type: general – SubjectFull: Police Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Face perception Type: general Titles: – TitleFull: Police sketch generator using generative adversarial networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Poornima, Ediga – PersonEntity: Name: NameFull: Tarang, Goggi Ruthvik – PersonEntity: Name: NameFull: Numaan, Syed Khasim – PersonEntity: Name: NameFull: Kashyap, Burra – PersonEntity: Name: NameFull: Seshappa, Angadi – PersonEntity: Name: NameFull: Ihassan, Mohammed – PersonEntity: Name: NameFull: Saini, Manisha – PersonEntity: Name: NameFull: Sehgal, Archana IsPartOfRelationships: – BibEntity: Dates: – D: 16 M: 04 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 0094243X Numbering: – Type: volume Value: 3263 – Type: issue Value: 1 Titles: – TitleFull: AIP Conference Proceedings Type: main |
| ResultId | 1 |