The Future Innovations of Biometric Facial Recognition

Enterprise Security Magazine | Wednesday, January 09, 2019

Biometric Facial RecognitionFacial recognition is a biometric software that maps an individual's facial features and stores the data as a faceprint. It uses deep learning algorithms to compare a digital image to the stored faceprint in order to verify the identity of an individual. Today it has become a viable option for authentication and identification. The uses include

• Greater accuracy
• Better security
• Smarter integration
• Convenient and frictionless
• Automation

Facial recognition continues to be the preferred biometric benchmark because it is easy to deploy and implement. Face detection and face match processes for identification are very fast and no physical interaction is required by the end user.

There are several biometric innovation projects. The top three application categories are

1. Facial recognition is used in security and law enforcement led by increased activity to combat crime and terrorism. It is also used in issuing identity documents, and often combined with other biometric technologies like fingerprints. Drones combined with aerial cameras also offer an interesting combination of facial recognition in monitoring mass events. Face biometrics can be employed in police checks also.

2. Facial recognition has made significant advances in the health sector too. It aids in

• tracking patient's use of medication more accurately
• detecting genetic diseases
• supporting pain management procedures

3. In marketing and retail by placing cameras in outlets, it is now possible to analyze the behavior of shoppers and improve the customer purchase process.

4. Social media platforms also have adopted facial recognition technology to diversify their functionalities to attract a greater user base.

When compared to other biometric techniques, face recognition may not be the most reliable and efficient. Quality measures are inevitable in facial recognition as a volume of variations is possible in face images. Factors like illumination, pose, and noise can affect the performance of facial recognition systems.