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Deep Learning Based Face Detection and Identification of Criminal Suspects

S. Sandhya1, A. Balasundaram2,*, Ayesha Shaik1

1 School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, 600127, India
2 School of Computer Science and Engineering, Center for Cyber Physical Systems, Vellore Institute of Technology (VIT), Chennai, 600127, India

* Corresponding Author: A. Balasundaram. Email: email

Computers, Materials & Continua 2023, 74(2), 2331-2343.


Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade. One of the most tedious tasks is to track a suspect once a crime is committed. As most of the crimes are committed by individuals who have a history of felonies, it is essential for a monitoring system that does not just detect the person’s face who has committed the crime, but also their identity. Hence, a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network (DNN) model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been proposed. After detection and extraction of the face in the image by face cropping, the captured face is then compared with the images in the Criminal Database. The comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity metric. After plotting the values in a graph to find the threshold value, we conclude that the confidence rate of the encoder model is 0.75 and above.


Cite This Article

S. Sandhya, A. Balasundaram and A. Shaik, "Deep learning based face detection and identification of criminal suspects," Computers, Materials & Continua, vol. 74, no.2, pp. 2331–2343, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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