Facial Recognition Algorithms for Machine Learning: Application and Safety

What is Facial Recognition

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.

How Facial Recognition Algorithm Works

Which algorithms are used in face recognition? Facial recognition is a complex task that requires numerous steps and complex engineering to complete. To distill the process, here is the basic idea of how the facial recognition algorithm usually works.

1. Your face is detected and a picture of it is captured from a photo or video.
2. 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 algorithm 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 key points 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.
3. The algorithm verifies your face by encoding it into a facial signature (a formula, strain of numbers, etc.) and comparing it with databases of known faces, looking whether there is a match. To improve the accuracy of a match, sequences of images rather than a single image are sent.
4. 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.

Uses and Potential Risks of Facial Recognition Algorithms

Fields of application of facial recognition for machine learning and AI 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 and for providing 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 deepfakes software, which swaps faces of individuals in videos, has already been used by a politician of India's ruling party to gain favor in elections. In China, facial recognition system mistook a famous businesswoman's face printed on the bus for a jaywalker and automatically wrote her a fine. Numerous studies in the USA and 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.

Why Quality Data Gathering And Data Labeling Matter

What are the possible solutions for such errors? 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 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 technology development. When images in the dataset are not 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 facial recognition development are:

• Get user's consent
• Do a thorough risk assessment
• Use anonymization techniques for big data

Despite its many flaws, facial recognition 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 data processing, 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 AI for facial recognition throughout our extensive experience in data labeling, 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.