5 Burning Questions About Biometric Face Verification

With the influx in technological innovations and advancements, almost every sector across this globe is facing some sort of identity theft and case chargebacks. According to the recent Javelin strategy and research, identity theft cost Americans a total of approximately $56 billion last year,  with almost 49 million consumers falling victim. Cybercrimes are evolving with the speed of light in this modern era of leading-edge technology. These progressing figures of identity theft cases lead to the fact that it is high time for businesses to incorporate AI-powered biometric face verification solutions to combat chargebacks. 

 

Reducing all the concerns which are related to facial recognition technology plays an effective role in the implication of policy making. Face verification technology prerequisites personal identifiable information, abbreviated as PII. continues enhancements in face recognition systems and revamping the way algorithms are trained and developed helps to combat any possibility of risk. 

 

Below are a few questions that address some of the leading concerns and helps in developing a public understanding of facial recognition technology. 

 

Question #1: Is There Any Difference Between Face Characterization And Face Recognition? 

 

Yes, face characterization is different from face recognition. Face characterization is basically an analysis that involves calcification of a single image on the basis of age, gender, emotions, and numerous other characteristics. On the other hand, face recognition technology involves the algorithms of artificial intelligence and machine learning incorporated in numerous sectors for the identification and comparison of faces in images and videos. Face characterization can be merged with face verification tools is a distinct technology with its own risks, development processes, and uses.  

Question #2: What Are The Risks of Utilizing Face Verification Technology In The Travelling Industry? 

Numerous countries incorporate facial recognition systems in cross borderings, airports, train stations, and more. Implementation of face identification systems has become a new normal in both private as well as government sectors. The risks of utilizing face verification technology encounter how data collection is performed and stores for the utilization of other purposes. Most of the people do not support the utilization of face verification technology for the prevention of those unpaid traffic tickets or other minor infractions, but they will support its incorporation for the identification of terrorists. Clean and transparent rules are required during the utilization of face verification online for data collection and storage for risk assessment. 

Question #3: Is the Face Recognition System Appropriate Enough For Law Enforcement? 

The best answer to this question is it highly depends upon what sort of utilization is envisioned and whether there exist transparent and clean rules governing that particular utilization of facial recognition. Immense evolution is observed in facial recognition technology over the past couple of years. Facial recognition systems have an extremely high rate of accuracy and efficiency in certain conditions. The best face verification algorithm had an error rate of just 0.1% as of December 2020. During the utilization of facial recognition technology, strong safeguards are mandatory. Legislations can justify such standards which are required for different law enforcement use cases, for example, recognition of the basis of body cams or pictures captured using mobile devices, real-time monitoring, and retroactive identification. 

Question #4: Is Face Identification Technology Giving Birth To False Arrest Risk? 

Out of 10 million arrests annually, there exist 3 reported cases for false arrest which are based in part on face verification. Facial recognition was utilized for the analysis of footage of crime for the generation of suspects on the basis of comparison with ID registries or criminal databases. The most important lesson to learn from these three reported cases is how crucial best training, as well as procedures, is for human investigators for risk assessment or misidentification from low-key searches or automation bias. Most of the risk which is associated with false arrest or misidentification can be reduced in the best possible manner in police departments and enhances standards that revolve around the utilization of facial recognition. 

Question #5: Is Biometric Face Verification Racially Biased? 

Shreds of evidence suggest that the demographic difference in facial recognition technology accuracy rate will eventually disappear with the progression in technological innovations. The national institute of standards and technology (NIST) came across the fact that the majority of the algorithms depicted significant demographic differences in the accuracy rate. NIST also came across numerous other conclusions, exhibiting that the differences between demographic groups were far less for highly accurate algorithms. This depicts that the effects of bias will decrease with the enhancement in face verification technology. The most fundamental factor that plays a promising role in reducing demographic differences in the selection of training data which is utilized for the development of algorithmic models. Other than that, demographic differences can also be decreased in enhancing the image quality. 

Conclusion

The latest technology, face recognition online is becoming a lightning rod for enormous social concerns. It lies at the convergence of legitimate as well as powerful concerns over policy, privacy, and artificial intelligence. The risks that are associated with the utilization of face verification technology can be managed effectively through the implications of safeguards and rules which are appropriate for each case.   It is mandatory to ensure that any new rule is not based on such information which is either outdated or incorrect. Hence technological advancements and innovations are never going to stop and the utilization of artificial intelligence in face recognition systems and applications will continue to grow at a swift pace in upcoming years.

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