SUSPECTSEARCH: AI-POWERED CRIMINAL IDENTIFICATION SYSTEM
| dc.contributor.author | Abukhanov, Batyrkhan | |
| dc.contributor.author | Abaideldinov, Tamerlan | |
| dc.contributor.author | Tsoy, Maxim | |
| dc.contributor.author | Gilazh, Bibarys | |
| dc.date.accessioned | 2024-06-18T06:29:10Z | |
| dc.date.available | 2024-06-18T06:29:10Z | |
| dc.date.issued | 2024-04-19 | |
| dc.description.abstract | The traditional approach to criminal investigation, taken in the context of rapidly developing technological advancements, presents itself to be rather outdated. One of the most crucial parts of identifying a suspect is criminal sketching. The modern process requires manual sketch craftsmanship from assets based on the description of the suspect in order to approximate the appearance of the culprit. This method is highly limited and burdensome, it requires a considerable amount of time and resources that could be used in other areas of law enforcement. The proposed system utilizes machine learning techniques to approach this problem and suggests an alternative way of identifying criminals. The system analyzes photos uploaded to the database and evaluates pre-defined metrics to synthesize a vector for a person that will be used to describe the appearance in abstract terms. The system then takes prompts from the intended users, which could be victims or witnesses, to synthesize a new vector for each prompt and will find the most corresponding photos from the database. The results of testing the product indicate decent accuracy and usability and propose a direct search from the database of people instead of a traditional empirical search from a manual approximated sketch. This method will remove the necessity for the time-consuming manual sketching process and further search for an imprecise image of a suspect. A simple and intuitive in-use interface will not require any special training of the police staff in order to integrate the product into the current system of law enforcement. The project might be limited as of today, but given enough time and resources, several additions will drastically improve the effectiveness of criminal investigation and contribute to the redistribution of the police force on other important tasks. | en_US |
| dc.identifier.citation | Abukhanov, B., Abaideldinov, T., Tsoy, M., & Gilazh, B. (2024). SuspectSearch: AI-Powered Criminal Identification System. Nazarbayev University School Engineering and Digital Sciences | en_US |
| dc.identifier.uri | http://nur.nu.edu.kz/handle/123456789/7877 | |
| dc.language.iso | en | en_US |
| dc.publisher | Nazarbayev University School Engineering and Digital Sciences | en_US |
| dc.rights | Attribution 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
| dc.subject | Facial Metrics | en_US |
| dc.subject | Criminal Identification | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | KNN | en_US |
| dc.subject | Law Enforcement | en_US |
| dc.subject | Identikit | en_US |
| dc.subject | Type of access: Open access | en_US |
| dc.title | SUSPECTSEARCH: AI-POWERED CRIMINAL IDENTIFICATION SYSTEM | en_US |
| dc.type | Bachelor's thesis | en_US |
| workflow.import.source | science |