The week's pick
Reseach Article
Recognition of Criminal Perpetrators using Multi Otsu Thresholding and Content-based Image Retrieval Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 7 |
| Year of Publication: 2024 |
| Authors: Suhenrro Y. Irianto, Sri Karnila, Adimas Aglasia |
10.5120/ijca2024923433
|
Suhenrro Y. Irianto, Sri Karnila, Adimas Aglasia . Recognition of Criminal Perpetrators using Multi Otsu Thresholding and Content-based Image Retrieval Approach. International Journal of Computer Applications. 186, 7 ( Feb 2024), 59-63. DOI=10.5120/ijca2024923433
Abstract
The development of human face recognition techniques is highly complex. multidimensional. and often subject to changes based on environmental and psychological conditions. The creation of a system is urgently needed and crucial to assist law enforcement. such as determining photos of faces suspected of being involved in criminal activities. With automated tools. it becomes possible to provide or display suspected faces in accordance with desired queries. In the legal domain. searching for the faces of criminals or fugitives is essential because not all criminal activities are captured by CCTV and other means. Therefore. sketch images based on eyewitness accounts are employed. Law enforcement typically seeks the assistance of skilled artists. especially facial sketch artists. to create facial sketches of criminal suspects based on information provided by eyewitnesses. even if only briefly observed. Developing a system for searching facial images using sketches by artists is immensely helpful in identifying criminal suspects and enables law enforcement to pinpoint individuals or groups under suspicion. Overall. out of 400 face images. 328 are correctly matched. and 72 are unmatched. The overall precision for the entire dataset is 82%. In This research employs two methods for creating a criminal face recognition system. namely. segmentation and Content-Based Image Retrieval (CBIR).
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