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“IDENTIFY” A Face Image Generation Approach for Identification of Criminals

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dc.contributor.author Hewavitharana, J.C
dc.date.accessioned 2022-03-11T08:51:28Z
dc.date.available 2022-03-11T08:51:28Z
dc.date.issued 2021
dc.identifier.citation "Hewavitharana, J.C (2021) “IDENTIFY” A Face Image Generation Approach for Identification of Criminals . BSc. Dissertation Informatics Institute of Technology" en_US
dc.identifier.issn 2017079
dc.identifier.uri http://dlib.iit.ac.lk/xmlui/handle/123456789/921
dc.description.abstract " Facial composite is one of the major fields in forensic science which helps the crime scene investigation officers to carry out their investigation process smoothly. A survey conducted in United States confirms that nearly 80% of the law enforcement agencies in USA use computer based automated systems to generate composites, not only in USA most other countries use advanced tools to carry out this task. Sri Lanka is far behind in the process of facial composite generation with a lot of inefficiencies and a lot of improvements to be made in the current process of manual sketching of facial composites. This research involves in automating the facial composite generation, eliminating the manual hand drawn process which is currently used in Sri Lanka. Despite the fact that there are many software used globally the application of these software will not assist in creating facial composites targeting Sri Lankan people as the facial features of the local population will not match the global facial feature templates. “IDENTIFY” provides an end-to-end process of generating high quality images with the help of state of the art GAN model. Users can evolve and mutate facial features when generating the facial composite as same as the existing procedure. This system will be very useful in criminal investigation as it is capable of generating high quality facial composites in less than half an hour. " en_US
dc.language.iso en en_US
dc.subject Classification Score en_US
dc.subject Convolutional Neural Network en_US
dc.subject Latent Vector Evolution en_US
dc.subject Adversarial Neural Network en_US
dc.subject Generative en_US
dc.subject Generative Model en_US
dc.subject Facial Composite Generation en_US
dc.title “IDENTIFY” A Face Image Generation Approach for Identification of Criminals en_US
dc.type Thesis en_US


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