TECHNOLOGY Facial Recognition Technology

Facial recognition is a technology capable of matching a human face from a digital image or a video frame against a database of faces to confirm an individual’s identity. Although less accurate than fingerprint recognition, it is often favored because of its contactless nature. It is mostly used in personal security, law enforcement, or digital onboarding in finance.

What is face recognition?

Facial recognition system is a sophisticated way to verify or ascertain someone’s identity using an algorithm that processes a digital image or video frame. It picks out distinguishing features of someone’s face shown in an image and matches these to the faces already logged within a database. It is growing in popularity, with new uses for it being developed all the time. Digital images and video stills are becoming clearer and easier to pick out distinct people and faces, while the matching software and algorithms are benefitting from increased data sources and accuracy.

The use of facial recognition and data is also increasingly becoming a vital part of commercial identification, helping to target individuals and personalize sales and marketing messages. Facial recognition is also increasingly used in automatic image indexing, human-technology interactions, and video surveillance systems. Technological advances are constantly happening, with significant upgrades.

What are the benefits of Facial Recognition?

There are many benefits to facial recognition software. A key area is improved security, both in public areas and for companies and organizations, such as banks, schools, prisons, and airports. It can help law enforcers to identify people of interest more quickly and work out their movements to track them down and prevent them from causing any harm.
It can also speed up identity checks in airports, at borders, and for personal admin tasks, such as banking or entering and exiting workplaces and other buildings. It also works very well with social media, providing an alternative, non-invasive method of accessing accounts rather than inputting passwords or codes. Retailers and marketing professionals can also use the technology to tailor advertisements and commercial messages to customers more precisely.

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How Facial Recognition Works

Former face recognition techniques were Voila-Jones Haar cascade or a histogram of oriented gradients. However, modern facial recognition technology is based on a specific neural network called convolutional neural network. To match the face templates, convolutional neural networks process each image through several steps:

Face Recognition Steps

  1. Face detection: The first step is detecting a face within a larger image or scene. This process involves distinguishing facial features from the surrounding environment and pinpointing their location within the frame.
  2. Face Analysis: Once a face is detected, the technology analyzes the facial features. This analysis is typically based on the geometry of the face, measuring various key points on the face, known as landmarks or nodal points, which can include the distance between the eyes, the shape of the jawline, and the contours of the cheekbones, lips, and nose.
  3. Feature Extraction: The analysis results in the extraction of facial features, which are used to create a faceprint or face template—a digital map of the face’s geometry.
  4. Comparison: This face template is then compared against a database of known faces. This is done using sophisticated matching algorithms that can handle variations in lighting, facial expressions, and angles.

A convolutional neural network converts every face pattern into a numerical code with every template expressed as a numerical vector. The closer two vectors are to each other, the more likely there is a face match between them.

Facial recognition technology operates by capturing a digital image or video frame that includes a face. This image is meticulously analyzed to identify specific landmarks or features on the face, known as nodal points. These nodal points, which are crucial elements of the individual’s facial geometry, such as the distance between the eyes or the width of the nose, are used to create a face template—a digital representation of the face’s unique characteristics.

Modern facial recognition systems employ sophisticated algorithms that can assess and log more than 80 such nodal points from the face template. The information from this face template is converted into a mathematical formula, resulting in what is termed a facial signature. This signature is a distinctive numerical code that encapsulates the facial features stored in the database. When facial recognition technology encounters a new image, it compares the new face template to the facial signatures within an existing database of known faces. Utilizing a complex array of artificial intelligence algorithms, the system evaluates the face template with impressive speed and accuracy to ascertain if there’s a match with any stored facial signature.

The key to the evolving success of facial recognition technology lies in advancements in machine learning, which refine the process of creating and comparing face templates. As the system learns from each face it processes, it incrementally increases its precision, reinforcing the technology’s pivotal role in areas such as security, personal authentication, and various applications across our digital ecosystem.

How facial recognition works

Brief History, and Evolution of Facial Recognition

In the 1960s, work was already being done on computer applications designed to measure and pinpoint facial features with a view to identifying them from a collection of faces on a database. Facial recognition as a concept, however, was formally pioneered in 1964 by computer scientists and mathematicians, Woody Bledsoe, Charles Bisson, and Helen Chan Wolf. They used manual mapping to explore the opportunities at first, however much of their initial work was never made public, due to the secret nature of its funding from an unnamed intelligence agency. 

By the 1970s, facial recognition accuracy was coming on in leaps and bounds with aspects such as hair color, lip shape, and other key identifying areas now included in the process. Linear algebra was used more and more in the decades that followed to hone the system even more finely. A key milestone came in 2001 when law enforcement in the US used facial recognition for the very first time to help identify Super Bowl spectators potentially causing disruption or harm.

Importance in Today`s Digital Ecosystem

Facial recognition systems are increasingly common in many, if not all aspects of modern-day life. In today’s rapidly evolving digital ecosystem, facial recognition stands out as a transformative technology, reshaping numerous sectors from security and finance to retail and entertainment. Its ability to swiftly and accurately identify individuals has not only streamlined authentication processes, eliminating the need for traditional passwords or physical IDs, but also paved the way for personalized user experiences. As businesses strive to offer tailored services and governments seek to enhance public safety, the significance of facial recognition continues to grow, highlighting its pivotal role in driving innovation and ensuring security in our increasingly interconnected world.

Facial recognition technology has burgeoned into a critical component of today’s digital ecosystem due to its ability to provide quick and reliable verification and identification services. In the current era where security and user verification are paramount, facial recognition offers a level of convenience and protection that traditional methods cannot match. In the realm of business, this technology has streamlined operations by enabling secure, keyless entry to facilities and by expediting user authentication processes, thereby saving time and reducing bottlenecks. In customer-facing sectors, facial recognition enhances user experience through personalized services and targeted marketing, creating opportunities for higher engagement and loyalty.

Moreover, in the wake of digital transformation, facial recognition is pivotal for online platforms, underpinning systems that manage digital identities and protect against fraud. Its integration into everyday devices, such as smartphones and laptops, has brought biometric security to the forefront of consumer technology, setting new standards for privacy and data protection.

The technology’s scalability and versatility also allow it to support a vast array of applications across various industries, from enhancing retail experiences with personalized advertisements to bolstering border security through advanced surveillance capabilities. Consequently, facial recognition stands as a cornerstone of innovation, driving forward a safer and more personalized digital future.

SmartFace Facial Recognition

Core Technology and Accuracy of Facial Recognition

Facial recognition technology’s core is anchored in its sophisticated ability to discern and identify individual faces from digital images and videos. This sophisticated process encompasses several stages: detecting a face, analyzing key features, extracting these features to create a faceprint, and finally, comparing it against a facial database for recognition. Underlying this sequence are pivotal infrastructures such as advanced artificial intelligence (AI), leveraging machine learning and deep neural networks, integral computer vision techniques for high-level image processing, and biometric analysis for capturing unique facial characteristics. When integrated into B2B systems, this technology not only enhances security but also streamlines operations, offering seamless user experiences across various digital platforms.

Core Technologies

  • Artificial Intelligence (AI): AI, particularly machine learning and deep learning algorithms, is at the heart of modern facial recognition systems. These algorithms enable the system to learn from vast amounts of data and improve over time.
  • Computer Vision: This field of AI is concerned with how computers can gain high-level understanding from digital images or videos. It involves the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images.
  • Biometric Analysis: Biometrics refers to the statistical analysis of biological data, which in the case of facial recognition is the unique features of the face.
  • Neural Networks: Particularly, convolutional neural networks (CNNs) are used for image recognition tasks and are crucial for the feature extraction phase in facial recognition systems.

Together, these technologies form the bedrock of facial recognition systems, allowing them to function with increasing levels of accuracy and reliability.

Facial Recognition Accuracy Is Improving

Facial recognition algorithms are not as accurate as fingerprint or iris algorithms. However, given the development of convolutional neural network, the accuracy of facial recognition algorithms has been on the uptick.

The accuracy of each algorithm is measured by two error classes:

False Positivity

False Negativity

False positivity occurs when the software wrongly considers photos of two different individuals to be the same person. On the other hand, false negativity means the software failed to recognize the same person. There is always a tradeoff between false positivity and false negativity, therefore, it can sometimes be difficult to set up the thresholds in biometrics.

Face recognition accuracy is rapidly increasing. The world’s leading face recognition algorithms have reduced error rates by orders of magnitude (from 10% to below 1%) in just 3 years. Between  2014 and 2018, the accuracy of facial recognition technology increased 20-fold. These continued improvements over the next several years will likely bring a myriad of new use cases. See Innovatrics results here.

Nist Results Facial Recognition Error Rate

Facial Recognition Technology Standards

The National Institute of Standards and Technology (NIST) is the leading international biometric testing organization and industry standard developer. NIST collaborates with other federal agencies, law enforcement, enterprise, and academic partners in order to:

  • Research measurement, evaluation, and interoperability to advance the use of biometric technologies including face, fingerprint, iris, voice, and multi-modal techniques
  • Develop common models and metrics for identity management, critical standards, and interoperability of electronic identities
  • Support the timely development of scientifically valid, fit-for-purpose standards
  • Develop the required conformance testing architectures and testing tools to test implementations of selected standards

The most prestigious industry test is the NIST Face Recognition Vendor Testing Program (FRVT), which accepts submissions from any organization worldwide. NIST FRVT assesses the capabilities of facial recognition algorithms for one-to-many identification and one-to-one verification. See Innovatrics NIST FRVT results.


Using Biometric Templates on Personal Devices

Biometric authentication is now used on personal devices instead of PIN or built-in within application as a verification or client onboarding tool. However, the main reason mobile phone manufacturers use it is due to its convenience. There is no need for password protection and management. Also, both fingerprint and face recognition are much faster than traditional authentication methods. There is also more security, because no one can simply look over the shoulder to steal your password. Finally, as there is match-on-device technology and the data is never provided to third parties nor is it stored in the cloud, the user can be sure no one else is tampering with it.

Using_Biometric on mobile phones

Algorithmic Biases

NIST has recently run a large-scale test focused on identifying biases in facial recognition technology. NIST found out that even the best algorithms still displayed a higher false positive rate among West and East African and East Asian individuals, while Eastern Europeans had the lowest false positive rate. The reason is demographic imbalances in the training data used to train these engines. Changing these imbalances is not easy – some are even built-in physics such as dark skin reflecting less light, and therefore, providing less detail to analyze. However, Innovatrics facial recognition algorithms are well trained for darker skin tones, according to NIST results, with real world confirmation on its Guinea election project.

In general, when preventing algorithmic biases, these methods can be used:

  • Better data labelling to ensure all groups of people are present
  • External dataset auditing – unbiased datasets make unbiased algorithms
  • Using de-biasing variational auto-encoders, which are able to automatically discover and mitigate hidden biases among training data
Biometric facial recognition-Algorithmic_biases

Applications and Use Cases of Facial Recognition

Nowadays, facial recognition technology has a broad spectrum of applications worldwide – from individual onboarding and personal security to the identification of individuals in crowds and gatherings. The most common use cases of this technology include:

  • Criminal identification of suspects
  • Digital onboarding of clients
  • Access control
  • Security and surveillance
  • Identity fraud prevention
  • Event registrations
  • Airport operations
  • Financial services verification
  • Smartphone unlocking
  • Retail analytics