Overview of Face Recognition

The information era has created a new setting where many of the rules are still being written. Any invention requires a careful assessment of legal and moral attention involved. The industrial revolution and technology have reshaped various areas which require identifications and recognition. With a well-established software, Face Recognition has become an exemplary identifier of individuals which helps in monitoring various operations of people. In the current society, security is improved, businesses enhanced, and frauds have minimalized since the introduction of the Face Recognition application. This paper will deliver an overview of the face recognition technology, an analysis of the advancement of the industry trends, and a discussion of how the technology is used and adapted by different industries.

Overview

Face recognition system refers to a biometric software or application with the capability of verifying or identifying individuals from video frames or digital pictures. The software operates by selecting the facial characteristics from an image and comparing with the features within the stored databases. Face recognition has been exemplary to safety systems and has been used in many biometric operations like in fingerprint and iris identification software. Traditionally, the recognition was conducted by extracting the important landmarks within a face and then compared manually to the images (Arya et al., 2015). With the current upgrade, it is possible to run as many images as possible and allow the system to choose only the matched photograph.

The system has loads of algorithms deployments globally for the last fourteen years and has prevented cheating in various ways. Primarily, live face identification in front of a camera is diminishing. Moreover, live video programs also monitor the features of an individual and can identify persons easily. Additionally, it helps classify gender making it easy to select the intended targets (Arya et at., 2015). The progression in the field of information and technology has integrated operations of businesses, hence, making the workers always do the right things. It has also lessened the occurrences of burglaries and murder as most countries have cameras in sensitive locations to help them monitor and protect the citizens. Face recognition has biometric software and maps personal characteristics accurately and stores the information as a faceprint. The deep learning processes compare digital imageries and live capture with stored data to help verify and identify individuals.

The Working of Face Recognition Technology

The system works by identifying the nodal points on a person’s face. About this, it recognizes eighty nodal parts considered as the endpoints which measure human face variables like width or length of the nose. It also gauges the cheekbone shapes and eye sockets depth. Therefore, the software captures information for nodal regions and stores the data as a faceprint. The stored facts then form the basis for captured face data in images and live videos. As a result, it makes it possible to identify the targets. The using of eighty points gives it a high probability of identification and recognition as it quickly analyzes the data with high accuracy levels (Ferguson, 2017). The only limitation is when the conditions may seem unfavorable like an obscured face or for insufficient light. The ability of the face recognition technology to identify individuals under favorable conditions qualifies it to be the best software which has helped monitor operations and prevented frauds. More so, it has improved the integrity of individuals as they work knowing that any slight mistake has evidence and can be analyzed to find them on the wrong side.

Advancement of Face Recognition with Industry Trends

Face recognition has been incorporated into the industrial trends globally. In 2014 alone, the face identification market was worth more than $1400 million, and there is an expectation of higher growths as the world progresses (“Facial Recognition Market-Global Industry Analysis and Forecast 2015-2022”, 2018). Estimated advancement rate between 2015 and 2012 stood at 9.5% due to increasing demands of the software. Today, robust technological progressions drive the facial recognition market as there is a requirement to improve surveillance operations by both government and civil agencies. The rising instances of terrorism, fraud identification, and criminal activities globally require adequate planning to stop. The fact that the system has the capability of scanning photographs in both 2D and 3D makes it ideal for surveillance (Ferguson, 2017). Moreover, web applications are increasing and require regular social interaction and image tagging. To drive the demand, it is projected that the incorporation of facial recognition would be effective for future use of these web applications. Therefore, the increased surveillance corporations and the websites industries require the software for efficiency in their operations.

Many global industries and dominant players like Safran Group, NEC Corporation, 3M Cogent Inc., among others are mostly biometric oriented Facial Recognition Market-Global Industry Analysis and Forecast 2015-2022”, 2018). They prefer facial recognition applications for their operations for more accountability. With the world increasing politically, some of these companies are given contracts to manage elections in various countries. For triumph operations, they prefer the recognition system to identify voters and to prevent any voter from balloting twice or more.

The current society requires close monitoring. Several areas in different nations have restrict4d movement due to security issues. Therefore, the advancements in technology require that an improved system is applied in such regions to control the people. Government utilities like the Department of Homeland Security and military use the technology (Ferguson, 2017). Moreover, new banking trends are emerging with the improved financial security needed. As a result, the increased demand for large and small corporates in making identifications make the technology one of the best in monitoring. As new business emerges, there is a belief that they will need the software and the application would generate millions of revenues.

Use of the Technology and Adaption by Various Industries

Face recognition is widespread. Various industries use and adapt different ways of using the software. It has been useful to identifications, healthcare, security, and marketing to mention a few. Primarily, most financial corporates use the algorithm within their systems. For example, Alibaba, MasterCard, and Amazon adapted the system and uses it to make face recognition payments known as selfie pay (Mahajan, 2016).

Biometric industries like Safran Group (OT-Morpho) and NEC Corporation among others use the software to store biodata about individuals (Whitehead & Carr-Archer, 2014). They have adapted the system to election management where they allow for recognition of voters through the fingerprints, iris identifier, and facial recognition. As a result, they remain accountable for any errors made during the electioneering process and have many countries have peaceful and credible polls.

Additionally, face technology has applied in many business areas like targeted publicity and receiving marketing feedback. For the advertisement, it helps in bringing more relevance to the clients and applies the technique to identify the gender affected by the advert (Mahajan, 2016). In marketing feedback, it is being used in identifying potential clients so that in case of offers, they are the first to receive.

Other fields of application include, mental health identification, reading concentration levels like emotions, and data securing. Moreover, it is widely used in unlocking devices like phones. There is also an increasing requirement for the face technology to be incorporated into unlocking cars to prevent car burglary incidences (Whitehead & Carr-Archer, 2014).

References

Arya, S., Pratap, N., & Bhatia, K. (2015). Future of Face Recognition: A Review. Procedia Computer Science, 58, 578-585. www.doi.org.1016/j.procs.2015.08.076

Facial Recognition Market-Global Industry Analysis and Forecast 2015-2022. (2018). Transparency Market Research.com. Retrieved on 2 March 2018, from www.transparency.marketrese...recognition-market-HTML

Ferguson, A.G. (2017). ”The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement.” NYU Press.

Mahajan, P. (2016). New Approach in Biometrics to Combat the Automated Teller Machine Frauds: Facial Recognition, International Journal of Engineering and Computer Science.

www.dx.doi.org/10.18535/ijecs/v5i5.22

Whitehead, N., & Carr-Archer, H. (2014). Real World Implementation of Facial Recognition Systems. Biometric Technology Today,2014 (4), 9-11. www.dx.doi.org/10.1016/s0969-4765(14)70071-2