STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Mechanical Dynamics and Systems Analysis Review Article

AI-Based Criminal Identification System A Breakthrough Approach

  1. K. Amrutha Kumari Department of Computer Science and Engineering, Gayatri Vidya Parishad College for Degree and PG Courses (A), Visakhapatnam
  2. L. Pratibha Department of Computer Science and Engineering, Gayatri Vidya Parishad College for Degree and PG Courses (A), Visakhapatnam
  3. S. Maharshi Department of Computer Science and Engineering, Gayatri Vidya Parishad College for Degree and PG Courses (A), Visakhapatnam
  4. D. Avinash Sai Department of Computer Science and Engineering, Gayatri Vidya Parishad College for Degree and PG Courses (A), Visakhapatnam
  5. M. Dinesh Department of Computer Science and Engineering, Gayatri Vidya Parishad College for Degree and PG Courses (A), Visakhapatnam

Abstract

Identifying and locating a perpetrator is a time-consuming and difficult process. The perpetrators are growing more skilled, leaving no biological evidence or fingerprint impressions at the crime scene. Using cutting-edge face recognition technology is a quick and easy solution. Through the use of linear programming, this research presents an innovative approach to classifying all face tracks collectively. In addition to the following, it incorporates: a novel method for extracting more informative data from aligned transcripts; a particular model for classifying background characters according to their face tracks; the implementation of new HOG (Histogram Oriented Gradients) face features; and a novel approach for labelling all face tracks simultaneously. The conventional systems implemented the CNN classification model for face detection, but due to the time-consuming process to detect face features, we are implementing the HOG algorithm to detect face features from Perpetrator Identification. Moreover, the SVM classifiers were used in the existing system for face recognition which is not detected effectively, therefore we are implementing the KNN classifier to recognize the faces effectively for the perpetrator identification.

Keywords

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