Although facial recognition technology has been around for a long, it has only just begun to gain popularity. Between 2023 and 2030, the global facial recognition market is anticipated to develop at a CAGR of 15% and reach USD 18 billion. This expansion is the result of more and more businesses using facial recognition technology for access control, fraud detection, attendance tracking, marketing, healthcare, and retail.
Since its debut, facial recognition technology has advanced significantly. Researchers began to lay the framework in the 1960s, albeit with limited resources. By the 1970s, progress in facial matching had been made. The NEC system saw the start of commercial use in the 1980s. In the 1990s, machine learning was widely used to improve accuracy. In the 2000s, marketing, legal, and security all experienced increased adoption. Phones, social media, and automobiles all underwent integration in the 2010s. The 2020s now usher in increased application expansion and greater precision. Early classifications, facial recognition, real-time tracking, and high accuracy IDs are examples of landmarks. The potential of facial recognition is still growing as this decade goes on.
Facial recognition has nearly 100% accuracy, making it a trustworthy identifying tool. However, there are still some issues that must be resolved, such as regulations, accuracy, and bias. Companies must give ethical use, privacy issues, and user approval top priority in order to meet these problems.
Facial recognition with AI enhancements has the potential to transform several sectors. AI-powered facial recognition is anticipated to boost security and efficiency by 2026, saving $5 trillion annually. To do this, though, businesses must concentrate on AI improvements that will have the biggest effects, like lowered bias and fairness, complicated pattern recognition, real-time flexibility, and improved feature extraction.
Bias is one of the main problems with facial recognition technologies. Algorithms for facial recognition may be biased against specific demographics, including women and people of colour. Inaccurate identification and discrimination may result from this. Reduced prejudice and fairness must be given top priority in business AI advancements in order to address this. Additionally, they can guarantee that the data sets they use are varied and accurately reflect the population. Accuracy is another issue with facial recognition technologies. Even though facial recognition is almost always accurate, there are still few instances where it might not be, as in dimly lit rooms or when people are wearing masks. Companies need to concentrate on AI improvements that increase accuracy, like sophisticated pattern recognition and feature extraction augmentation, to address this.
Despite the potential advantages of facial recognition technology, ethical issues must also be taken into consideration. Consumers' and employees' agreement to the use of facial recognition technology must be verified by businesses. The GDPR and CCPA are just two examples of the rules that have an impact on the use of facial recognition technology. To prevent legal problems, businesses must strictly follow these requirements. There are explicit guidelines for the use of facial recognition technology in the GDPR and CCPA. To prevent legal problems, businesses must strictly follow these requirements.
In conclusion, facial recognition technology has advanced significantly, and in the years to come, more people will start to use it. To realise its full potential, businesses must deal with issues including bias, accuracy, and regulations. Companies can make sure that facial recognition technology is utilised sensibly and for the good of all by giving ethical use, privacy issues, and user acceptability top priority.
This article is based on the webinar "Beyond Faces: Accelerating Business Success with AI Facial Recognition" About the speakers:
Paul G. Savluc is an AI expert, machine learning specialist, and deep learning professional with a strong background in IT. He is passionate about harnessing the power of data through AI, ML, and DL, and has a unique blend of skills in data engineering and data analytics. Paul is recognized for his ability to design and implement robust embedded systems, and excels at creating data pipelines, ensuring data integrity, and using cutting-edge analytics tools to unearth hidden patterns and trends. He is currently working as a Data Wrangler at Moment Factory, where he has contributed to high-profile projects for globally renowned clients such as FIFA, CFG Bank, and leading international casinos.
Ivan Draganov is the Head of Growth at LyRise.AI, where he spearheads efforts to connect companies with the right AI talents, ensuring a seamless match that fosters efficient collaboration and maximizes their impact. Previously, he worked as a Senior Innovation Analyst at Dealroom.co, where he worked closely with governments, corporates, and venture capital funds to discover and track technology trends. Ivan has delivered keynote speeches at prestigious events such as Techsylvania, Deep Foodtech Conference, Edtech roundtable by LocalGlobe, ICMA, EduData Summit, Icos Capital Innovation Summit, AgroCode, AgriFood 2021, and more.