Case Study: Unmasking Criminals: Advanced Policing Through Facial Recognition Technology

Facial Recognition

Case Study: Unmasking Criminals: Advanced Policing Through Facial Recognition Technology


Our client is a government agency, busy law enforcement department, in a large metro city in India.

Business Problem:

  • The agency was confronted with escalating crime rates and was in dire need of modern solutions to expedite the identification and apprehension of criminals. Traditional methods were proving to be time-consuming and less efficient in solving cases, especially those that had gone cold over the years.


The implementation of Facial Recognition Technology (FRT) was seen as a game-changer. The agency employed FRT to scan and match facial data against criminal databases and surveillance footage to identify suspects or locate missing persons. Personnel were trained to operate the technology efficiently, and measures were taken to minimize system bias and improve accuracy.


A notable success was witnessed in 2022 when the FRT identified a murder suspect who had eluded capture for many years. This case underscored the technology's potential in revitalizing cold case investigations and enhancing the overall efficiency of the law enforcement process. Furthermore, the technology facilitated a higher rate of crime detection and prevention, significantly contributing to public safety. However, challenges such as privacy concerns, accuracy, and system bias emerged as areas requiring further attention and improvement.

Technology Used:

  • Microsoft Azure Face API: This cloud-based service was utilized to provide advanced facial recognition algorithms for identifying and verifying individuals from images and video feeds.
  • Database Management Systems (DBMS): Microsoft SQL Server was employed to manage the vast repository of facial data efficiently, ensuring quick data retrieval and analysis.
  • Machine Learning Algorithms: Microsoft Azure Machine Learning was leveraged to train the Facial Recognition system, aiming to improve accuracy and reduce biases in identification processes.
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