Context: Police history has shown up fault lines in law enforcement strategy in discharging the twin tasks of maintenance of peace in public places &  crime prevention and detection.

  • Underperformance in crime prevention and detection
    • Poor success rates in solving crime: it is between 30% and 40% globally. Crime using knives continue to worry about London’s Metropolitan Police, while the frequency of gun violence is high in U.S. cities. 
    • Except in sensational cases which have attracted media attention, the Indian police have also been guilty of underperformance.
  • Concerns:
    • Preventing crime is arduous because of the dimensions and complexities of modern society, solving a crime is relatively easy.
    • Safety Vs. Privacy: Citizens demand newer crime control measures which will keep them safe. At the same time, they oppose smarter police innovations like counter-crime facial recognition technology because of perceived danger to individual rights and privacy. 

Why facial recognition technology should not be used?

Why facial recognition technology should be used?

Software bias: The suggestion is that there is a disproportionate number of black and non-white faces captured by facial recognition software.

The cameras are meant to take pictures at random rather than of specific segments of the population. 

Privacy violation: Increased use of CCTV cameras in a number of public places is in a sense a threat to anonymity. 

If there is no match of a face with existing records, the data is deleted by the police. 

Facial recognition could lead to online harassment and stalking. 

Citizens gladly hand over their data to private companies, especially while unlocking phones using one’s fingerprint. 

Mistaken identity: A study by the National Institute of Standards and Technology (NISDT) in the U.S. found that it may not be 100 per cent accurate. What if the police think the suspect is you?

Useful technology: Just as DNA testing establishes either the guilt or the innocence of a person arraigned for crime, facial recognition performs an equally vital role in criminal justice administration. 

Way forward

  • Using varied data: The US study suggests that error rates could perhaps be brought down by using a diverse set of training data. 
  • Training to handle digital evidence: Better training, care and sophistication in the criminal investigation will address the fears about privacy.

In the ultimate analysis, any modern technology is fraught with hidden dangers. There is no claim of infallibility either by the software maker or by the person selling it or who advocates its deployment. 

About Facial recognition Technology

  • Face recognition systems use computer algorithms to pick out specific, distinctive details about a person’s face.
  • Every face has numerous, distinguishable landmarks, the different peaks and valleys that make up facial features also called nodal points. 
  • Each human face has approximately 80 nodal points. Some of these measured by the software are:
    • Distance between the eyes
    • Width of the nose
    • Depth of the eye sockets
    • The shape of the cheekbones
    • The length of the jaw line
  • These nodal points are measured creating a numerical code, called a faceprint, representing the face in the database.
  • 3D model: Capturing a real-time 3D image of a person's facial surface, 3D facial recognition uses distinctive features of the face -- where rigid tissue and bone is most apparent. These areas are all unique and don't change over time.
  • Surface Texture Analysis, works much the same way facial recognition does. A picture is taken of a patch of skin, called a skinprint. By combining facial recognition with surface texture analysis, accurate identification can increase by 20 to 25 percent.

Image Source: Arizona Department of Transportation