Once a feature of science fiction, facial recognition technology has become an integral part of modern life. From unlocking smartphones to enhancing airport security, it’s hard to imagine a world without it. What makes this possible is the expanding sophistication of Artificial Intelligence (AI) and its subsets, such as Machine Learning (ML), Deep Learning (DL) and Generative Adversarial Networks (GANs). Facial recognition, specifically, owes much of its success to DL – a form of AI designed to mimic human brain functions to process vast amounts of data. But just how does facial recognition work? Below we’ll take a closer look at the tech.
In development since the 50s, AI’s time has come
AI’s journey began in the 1950s with simple attempts to create machines capable of “thinking.” Over the decades, it evolved into a sophisticated field encompassing various technologies. Facial recognition, a product of this evolution, leverages Deep Learning algorithms to identify unique facial features and match them against stored data.
Modern facial recognition systems analyse billions of data points almost instantly, making it one of the most advanced applications of Artificial Intelligence today. From aiding law enforcement to personalising customer experiences, its use cases are as diverse as its capabilities.
How facial recognition works
Facial recognition showcases the power of AI and DL in ways that were unimaginable even two decades ago. By analysing facial features and patterns, this technology has found applications in security, retail, healthcare and more. Here’s a step-by-step breakdown of how facial recognition works:
– Image acquisition
- The process begins with capturing an image of a face through a camera, whether on a smartphone, CCTV or specialised device.
- Example: A smartphone’s front-facing camera takes a photo when you attempt to unlock it.
– Face detection
- Once an image is captured, the system identifies the presence of a face within it.
- Algorithms scan the image for facial patterns, distinguishing the face from other objects or backgrounds.
- Example: Facebook’s ability to detect faces in uploaded photos and suggest tags.
– Alignment
- Detected faces are aligned to ensure consistency, regardless of the face’s angle or orientation.
- The system adjusts features like eyes and nose to fit a standardised frame, enabling better comparison.
- Example: Facial recognition aligning a tilted headshot to a front-facing template.
– Feature extraction
- This is where Deep Learning comes into play. Unique features such as the distance between the eyes, the shape of the jawline and the curve of the lips are mapped out and converted into a mathematical representation, known as a facial signature.
- Example: Identifying distinguishing features in a person’s face while ignoring temporary elements like glasses or hats.
– Face matching
- The facial signature is compared against a database of stored signatures. These databases can belong to law enforcement, social media platforms or personal devices.
- Example: Unlocking a phone when the system matches your face to its stored profile.
– Machine learning algorithms
- As the system processes more data, it uses Machine Learning (ML) to learn and improve its accuracy over time. Advanced models use neural networks to identify subtle variations and improve performance.
- Example: A security system becoming more adept at recognising authorised personnel as it “learns” their features.
– Decision-making and output
- Finally, the system decides whether the scanned face matches an existing profile. This decision is outputted as an action, such as unlocking a device or granting access to a secured area.
- Example: A “face not recognised” alert at an airport security checkpoint.
– Enhancements through DL and AI
- Advanced DL models add layers of intelligence by factoring in context, such as recognising faces in poor lighting or spotting changes due to ageing.
- Example: Facebook’s AI suggesting old photos of you with remarkable accuracy despite changes in appearance over time.
Controversy surrounding facial recognition
Facial recognition technology has rapidly integrated into our everyday lives, from the convenience of unlocking our mobile phones to critical security measures at airports and even tracking criminals. It’s a testament to how far AI has come and the limitless possibilities it offers.
Yet, as with all AI-driven innovations, facial recognition is not without controversy. Privacy advocates worry about mass surveillance, while others fear the misuse of data. These concerns are compounded by issues like racial bias and false positives, which can undermine trust in the technology.
The future for facial recognition
Despite scepticism, facial recognition is here to stay. As its accuracy and applications continue to improve, resistance to this technology becomes less about whether it should exist and more about how it’s implemented responsibly. It’s already shaping industries like healthcare (diagnosing rare disorders via facial features), retail (personalised shopping experiences – think Minority Report) and security (identifying threats in real time).
The takeout
Facial recognition represents just one facet of AI’s transformative power. Love it or loathe it, we are living in a world where these systems are becoming indispensable. The best path forward is to ensure ethical usage while embracing the benefits it brings. As AI continues to evolve, facial recognition will undoubtedly remain a key driver in the technological landscape.
