Fingerprint Recognition for Crime Scenes Using Deep Learning

Authors

  • P. Mani Bharadwaj B.E. Scholars, Dept. of Electronics and Communications Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana,India
  • J. Sai Teja B.E. Scholars, Dept. of Electronics and Communications Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India
  • P. Srivally B.E. Scholars, Dept. of Electronics and Communications Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India
  • B. Sai Reddy Associate Professor, Dept. of Electronics and Communications Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana

Keywords:

CNN, hybrid machine, open cv, python, and Random forest

Abstract

Crime-scene fingerprint photos are crucial hints for resolving ongoing cases. Using deep machine learning and convolutional neural networks, we provide a comprehensive crime scene fingerprint identification method in this research (CNN). Precision photography and sophisticated physical and chemical processing techniques are used to collect images from crime scenes, which are then kept as databases. It can be challenging to categorize the photographs taken from the crime scene because they are frequently insufficient. The fingerprint images need to be pre-processed using the appropriate enhancement techniques, and features are then extracted from the fingerprint images. Pre-processed data features are sent into the CNN as training and testing input. According to testing findings on the database using Open CV-Python, it is 80% accurate to identify a partial or complete fingerprint in the criminal database

References

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Published

2022-07-26

Issue

Vol. 12 No. 2 (2022)

Section

Review Articles