- How Does a Facial Recognition Algorithm Work?
- The Main Uses and Potential Risks of Facial Recognition Algorithms
- Why Quality Data Gathering and Data Labeling Matter
- Concluding Thoughts
- FAQ
By 2032, the global AI market is predicted to reach about USD 2,575.16 billion. One of the most rapidly developing AI technologies, besides being among the most controversial ones, is face recognition using machine learning. In some places today, people can use their face to authorize purchases of food or get into their apartment, while in others the use of facial recognition technology is forbidden altogether. How can facial recognition technology be beneficial and also harmful? We’ll try to answer this question in detail throughout this article.
By definition, facial recognition is a technology capable of recognizing a person based on their face. It is grounded in complex mathematical AI and machine learning algorithms which capture, store and analyze facial features in order to match them with images of individuals in a pre-existing database and, often, information about them in that database. Facial recognition is a part of a tech umbrella term of biometrics that also includes fingerprint, palm print, eye scanning, gait, voice, and signature recognition.
An alternative option is image-based neural network face recognition, which is comprehensive and can automatically find and extract faces from the whole image, offering a highly accurate and efficient solution in the realm of biometric technology. This group of approaches, referred to as deep face recognition technology, includes deep convolutional neural networks (CNNs) and ensures cutting-edge performance and enhances the overall security landscape.
How Does a Facial Recognition Algorithm Work?
Machine learning algorithms for face recognition are trained on annotated data to perform a set of complex tasks that require numerous steps and advanced engineering to complete. To distill the process, here is the basic idea of how the facial recognition algorithm usually works.
- Your face is detected and a picture of it is captured from a photo or video.
- The software reads your facial features. Key factors that play a role in the detection process can differ from each other based on what mapping technique the database and ML algorithm for facial recognition use. Commonly, those are either vectors or points of interests, which map a face based on pointers (one-dimensional arrays) or based on a person’s unique facial features respectively. 2D and 3D masks are utilized for this process. It’s common to think that keypoints are used for best facial recognition software, but in reality they are not descriptive or exhaustive enough to be a good face identifier for this task.
- The algorithm verifies your face by encoding it into a facial signature (a formula, strain of numbers, etc.) and comparing it with a database of recognizable faces, looking whether there is a match. To improve the accuracy of a match, sequences of images rather than a single image are sent.
- Assessment is made. If your face is a match to data in the system, further action may be taken, depending on functions of facial algorithm software.
There are many ready-made face recognition algorithms written in Python, R, Lisp or Java, though, depending on the time and budget available, many engineers choose to custom-make them to fit specific research or business purposes.
For an advanced face recognition system (or image recognition, in general), consider leveraging expert computer vision services to optimize the performance and accuracy of your model, unlike NLP services, which are best suited for tasks requiring text or audio data.
The Main Uses and Potential Risks of Facial Recognition Algorithms
The fields of application of a face recognition system using machine learning algorithms are plenty. The most common ones are related to:
- Security and surveillance (law enforcement agencies or airports),
- Social media (selling data, personalization),
- Banking and payments,
- Smart homes,
- Personalized marketing experiences.
Although, it is not the whole picture. There are more subtle ways in which face recognition algorithms are changing our everyday life in meaningful ways too, proving that this technology is still far from infallible.
A famous deepfake software, aka face recognition technology based on deep learning, swaps faces of individuals in videos. It has already been used by a politician of India's ruling party to gain favor in elections. In China, a facial recognition system mistook a famous businesswoman's face printed on the bus for a jaywalker and automatically wrote her a fine. Also, numerous studies in the USA and the UK proved that facial recognition AI has significant troubles recognizing non-white faces, is often biased on gender and identifies “false positives” the majority of time, increasing probability of grievous consequences.
This begs the question, is facial recognition safe for us?
Why Quality Data Gathering and Data Labeling Matter
What are the possible solutions for potentially unsafe facial authentication in AI? How to make sure that facial recognition software is safe to develop and utilize? One thing we know for sure — there are two processes that matter the most in the development of an AI. These are data collection and data labeling.
Both high-quality data and secure data labeling solutions have a dramatic impact on AI technology development. When annotations in the image or video dataset are not of high quality, not diverse enough or have too many errors, even the best technology falls short. Additionally, when dealing with large amounts of sensitive data, its usage, access, or even a potential breach — all are serious issues that must be accounted for.
It gets even more complicated with GDPR or CCPA. Data privacy and security legislation indeed protect individuals and expand their rights. They are also quite restrictive to the types of biometric data allowed to collect or analyze, so ensuring compliance for projects that involve images of faces can be quite tricky. Three most important tips to avoiding legal trouble in biometric facial recognition development are:
- Get the user's consent;
- Do a thorough risk assessment;
- Use anonymization techniques for big data.
Concluding Thoughts
Despite its many flaws, facial recognition and machine learning does not seem to stop being researched for academic or other purposes. In today’s digital world, guaranteeing that facial recognition technology is safe and secure must be an important theme for governments, lawmakers and among the top priorities of the developers themselves.
When it comes to preparing data for such systems, specialized companies can enhance your workflow by helping you eliminate the lengthy processes of organizing, cleaning and categorizing your datasets. Understanding the complex premises of using machine learning in facial recognition, we at Label Your Data offer high-quality and secure data labeling solutions, which are certified with top industry security standards (ISO 27001, PCI DSS). Moreover, all of our hardware, software, and processes for data labeling are GDPR-compliant.
FAQ
Which algorithm is used for face recognition in machine learning?
In facial recognition, Convolutional Neural Networks (CNNs) are considered the best algorithm for this face identification method due to their ability to effectively extract features and identify faces in images.
How do machine learning algorithms function in face recognition?
In face recognition, ML algorithms analyze and process facial features from images or videos, allowing the system to learn patterns and characteristics unique to each individual through training artificial neural networks. They can identify or verify individuals by matching these learned patterns against new facial data.
What language is used for a machine learning model to detect people using facial recognition?
The language used for a machine learning model to detect people using facial recognition is typically Python. However, if we talk about image recognition, TensorFlow, a popular machine learning library written in low-level C/C++, is used for real-time image recognition systems, offering a rich collection of AI libraries and tools.
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