Face recognition is one of the fastest growing technology these days, and there are various companies not only involved in R&D but same time developing numerous applications in different fields.
There have been various biometric ways in use to recognize a person’s identity like finger-scan, hand-scan, retina-scan, and face recognition. Face recognition is not an altogether new technology, but artificial intelligence and machine learning techniques are continually making it better. It is the latest way to identify people. Face recognition is rapidly gaining momentum with many business benefits such as enhanced user experience, cost-effective without manual intervention. In some cases, people can be recognized even without his/her knowledge by taking his live photo or video.
How does it work?
Face recognition uses deep learning algorithms which is an advanced form of machine learning, to compare a digital image to the stored faceprint to verify an individual’s identity.
Every human face has approximately 80 nodal points that are nothing but the peaks and valleys that make up the different facial features and help to distinguish individuals. These nodal points are measured for each face and create a numerical code, called a faceprint, representing the face in the database.
Some of the features measured by Face Recognition Technology are:
- The distance between the eyes
- The width of the nose
- The shape of the cheekbones
- The depth of the eye sockets
- The length of the jawline
In Face recognition application, these measurements are retained in an application database and used as a comparison for any person image needed to be identified.
Use Cases:
While the use case of face recognition can be endless, here are a few that are in production already and widely being used or being researched most –
- Human face-based Attendance Management System for any company
Implemented in Tavant and being used as a pilot project
- Identify suspicious person at any public places or restricted places
Already deployed at some of the airports/subways in Japan, UAE, and China
- Face recognition-based check-in for better customer experience and save time
Recently Delta airlines rolled out face recognition technology at Detroit Metro Airport
- Unlocking mobile, PC or any personalized device
For example, Apple’s iPhone X includes Face ID technology that allows users to unlock their phones with a faceprint mapped by the phone’s camera.
- Entertainment Industry
For example, the Kinect motion gaming system leverages face recognition to differentiate among players.
- Targeted Advertisement
Smart advertisements in airports are now able to identify the gender, ethnicity, and approximate age of a passer-by and perform targeted advertising according to the person’s demographic.
- Improved User Experience
MasterCard, Amazon, and Alibaba have rolled out face recognition payment methods often referred to as selfie pay
Technology Adoption Challenges:
With every technology adoption, in addition to benefits, there are a couple of challenges as well which needed to be known before implementing any use case:
- Security: Your facial data can be easily gathered and likely to be stored privately or in the public domain, often without your knowledge and permission. It is possible that hackers could access and steal this data and may misuse it.
- Privacy: Face recognition technology is being used more widely. That means your facial data could end up available in a lot of places. You probably even would not know who has access to it.
- Freedoms: Government agencies and even unauthorized entity may track your personal data. It would not be easy to stay anonymous.
- Safety: Also, face recognition can also be misused and lead to online harassment and stalking. For example, what if someone takes your picture in public and uses face recognition software and finds out exactly who you are.
- Mistaken identity: Face recognition data can be prone to error, i.e., misidentify or failing to identify, even with very low probability, which can implicate people for crimes they haven’t committed.
- Limitation: Face recognition application cannot differentiate identical twins. Also, it will not give the desired output in dim light.
The Road Ahead:
Face recognition technology is still in an evolving phase and gradually being adopted in various applications ranging from social media to critical government applications. Many top players like Amazon, Google, and IBM have come up with their offerings that can be used by anyone and investing in research to improve the speed and accuracy of the process. Companies must unleash the potential of face recognition technology and its implementation applicability to gain a better competitive advantage in their business.
