Next in this series on physical and behavioral biometric technologies, Ravi Das continues on the topic of facial recognition technology. In the previous article, the author described the various facial recognition models and techniques—and the scientific and mathematical factors that support them. Now he explains how to evaluate a facial recognition system as well as the advantages and disadvantages of this technology.

Facial recognition systems can be evaluated against a set of criteria, but there are differences between it and the other biometric technologies. While the face may not offer information as unique as do the iris and retina, facial recognition technology can be very scalable. And, just as with fingerprint recognition and hand geometry recognition, facial recognition can fit into a wide variety of application environments.

The evaluation of facial recognition can be broken down as follows:

  • Universality: Unlike all other biometric technologies, every individual possesses a face (no matter what the condition of the face is in), so at least theoretically, it is possible for all end users to be enrolled into a facial recognition system.
  • Uniqueness: Facial recognition is not distinctly unique at all, since members of the same family, especially identical twins, can genetically share the same types and kinds of facial features. (When it comes to the DNA code, it is the facial features which we inherit the most resembling characteristics from.)
  • Permanence: Given the strong effect of weight gain and weight loss (including the voluntary changes in appearance), as well as the aging process we all experience, permanence of the face is a huge problem. In other words, the face is not at all stable over time, and can possess a large amount of variance. As a result, end users may have to constantly be re-enrolled in a facial recognition system time after time, thus wasting critical resources and processing power.
  • Collectability: The collection of unique facial features can be very difficult, due to the vast differences which can occur in the environment in which the image is acquired. These include variations in lighting, lighting angles, and the distances at which the raw images are captured, as well as extraneous variables such as sunglasses, contact lenses, eyeglasses, and other types and kinds of facial accessories.
  • Performance: Facial recognition performance has both positive and negative aspects, which are as follows:
    • Accuracy: Facial recognition according to recent research, has a False Acceptance Rate (FAR) of .001, and a False Rejection Rate (FRR) of .001.
    • Backward compatibility: Any type or kind of 2-Dimensional photograph can be added quite easily to the database of the facial recognition system, and subsequently utilized for identification and verification.
    • Lack of standardization: While many facial recognition systems exist, there is a severe lack of standards amongst the interoperability of these systems.
    • Template size: The facial recognition biometric template can be very large, up to 3,000 bytes, and as a result, this can greatly increase the storage requirements as well as choke off the processing system of the facial recognition system.
    • Degradation: The constant compression and decompression and recycling of the images can cause serious degradation to the facial images which are stored in the database over time.
  • Acceptability: In a broad sense, facial recognition can be widely accepted. However, when it is used for surveillance purposes, it is not at all accepted, because people believe that it is a sheer violation of privacy rights and civil liberties. Also, some cultures prohibit the use of facial recognition systems, such as the Islamic culture, where women are required to wear head scarves and hide their faces.
  • Resistance to circumvention: Facial recognition systems can be very easily spoofed and tricked by 2-Dimensional facial images.

Up Next: The last few articles in this series have provided an in-depth look at facial recognition technology. In the next article, the author will move onto yet another controversial biometric technology: retinal recognition.

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Ravi Das is a Cybersecurity Consultant and Business Development Specialist. He also does Cybersecurity Consulting through his private practice, RaviDas Tech, Inc. He also possesses the Certified in Cybersecurity (CC) cert from the ISC2.

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