Facial Recognition

Facial Recognition

Face recognition in real-time is a part of the biometrics field. It involves the use of a device to take a video or an image of an individual’s face and compare it to image models available in any database. Face recognition starts with the step of taking pictures of an individual from different angles. The verification process requires a person to stand in the view scope of a camera for a short moment for the images to be compared. Usually, face recognition is kore useful in facial authentication than identification, mainly because someone can alter their face using masks (Owayjan et al., 2015; Galbally et al., 2019). When combined with other biometric methods, face recognition can help improve identification and verification results. This paper aims to determine the usefulness of facial recognition, how it is implemented, and how organizations can use face recognition to achieve competitive advantage.

The Usefulness of Facial Recognition

Facial recognition technologyimportance has been proved in its application in different aspects to individuals, groups, corporation and governments. Facial recognition technology has played a vital role in enhancing security. From a personal level, facial identity technology today is being applied in locking important devices such as phones, computers or home safe box or important rooms for individuals. Thus, facial recognition for personal use protects private property such as a home, room, phone or computer. At a corporate level, facial recognition help in surveillance and maintaining organization security. Facial recognition technology is used in surveilling the people leaving organizational facilities, making it easy to track trespassers and criminals. At the government level, security agents like police and army use facial recognition technology to track terrorists. Facial identity technology is mostly used forfacial comparison purposes in companies and security agencies (Galbally et al., 2019). Facial comparison is useful tocompanies and government as it is used to recognize the identity of people through comparing new faces to the data available in the system..

Facial recognition has enabled the process of identity automation. Facial recognition technology integration in the company and national levels play a significant role in the automation process. Identification automation makes it possible to identify people through the system. Automation of identification has enabled fast and accurate identification of people compared to the manual identification process, which was alow and did not boast high accuracy. According to Husken et al. (2005), 3D facial recognition technology has boosted accuracy, making it easy to recognize a face, and it is not easy to fool the technology.

Security software readily accepts the installation of facial identification technology.  Facial recognition technology has seamless integration features which are beneficial to companies as they do not have to undergo extra cost in the integration process.

Implementing Facial Identification Technology

The implementation process of facial identification technology involves various about three significant steps. Once an entity has decided to acquire facial identity technology, a choice is made on providing company. The providing company does the installation of facial recognition technology. The providing company also train the IT team on how to use the technology. Different facial recognition technology is implemented in varying ways and also work differently. After installation and training, the facial recognition technology undergoes the testing process. An example of how facial identity is used in a company to maintain information security is when companies use the technology to verify the user’s identity periodically. If the authenticated user face completely disappears from the system performs a screen lock or completely shuts down(Xiao & Yang, 2010). The approach ensures that nobody else can use an information system except the authorized persons, thus securing the entity’s information.

How Organizations Use Facial Recognition to Achieve Competitive Advantage

Organizations use Facial recognition to protect data that can be used by competitors. Some devices allow the use of face recognition to protect data that is impossible to protect from hackers when using passwords.

Face recognition also helps in retail and marketing. Retailing and marketing are the areas where face recognition is least expected, yet it is very promising. Knowing one’s customer is essential and combined with modern marketing strategies to improve the experience of customers. Through placing cameras in stores, one can be able to analyze the customers’ behavior and improve the purchase process of the customer. The sales employees have access to the information of the customers that are included in their media platforms to produce responses that are customized. Stores such as Amazon GO already use the system.

Facial recognition can be used by organizations to create a personalized customer experience that can make an organization in a better business position. Through face recognition, information can be associated with the face of a person, which can be detected when a person is present. Face recognition helps the organizations to create a frictionless customer experience through identifying the customers’ interests and preferences serving them better. Customers are encouraged to return to a place where they are made comfortable, and their needs are well met.

Face recognition leads to increased revenue, which organizations can use to increase production and improve services, achieving competitive advantage (Masupha et al., 2015). As a result of security and personalized customer services and experiences, customers are likely to visit the organization again, which can lead to an increase in revenue. Revenues help organizations to maintain their operations and avoid shutdowns.

References

Bobde, M. S. S., & Deshmukh, M. S. V. (2014). Face Recognition Technology. International Journal of Computer Science and Mobile Computing, 3(10), 192-202.

Galbally, J., Ferrara, P., Haraksim, R., Psyllos, A., & Beslay, L. (2019). Study on Face Identification Technology for its Implementation in the Schengen

Husken, M., Brauckmann, M., Gehlen, S., & Von der Malsburg, C. (2005, September). Strategies and benefits of fusion of 2D and 3D face recognition. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)-Workshops (pp. 174-174). IEEE.

Masupha, L., Zuva, T., Ngwira, S., & Esan, O. (2015, December). Face recognition techniques, their advantages, disadvantages, and performance evaluation. In 2015 International Conference on Computing, Communication, and Security (ICCCS) (pp. 1-5). IEEE.

Owayjan, M., Dergham, A., Haber, G., Fakih, N., Hamoush, A., & Abdo, E. (2015). Face recognition security system. In New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering (pp. 343-348). Springer, Cham.

Xiao, Q., & Yang, X. D. (2010). Facial recognition in uncontrolled conditions for information security. EURASIP Journal on Advances in Signal Processing, 2010, 1-9.Information System.